Without requiring parity-time (PT) symmetric optical potential, we explore a memory-based temporal synthesis mechanism for non-Hermitian photon control using electromagnetically induced transparency (EIT). In an N-type EIT system, a dynamic double-lattice configuration can be scheduled to sequentially impose spatially shifted periodic cross-phase modulation and cross-amplitude modulation on the spin excitation via giant Kerr nonlinearity. Passive PT-symmetric imprinting can thus be temporally synthesized in coherent optical memory at low light levels, whereby the PT phase transition of the system is flexibly engineered in the time domain. Using the multiple exposure technique, the PT-symmetric moiré imprinting could be further synthesized to control several equal-intensity diffraction peaks. Our work holds promise for time-dependent non-Hermitian manipulation of the retrieved light.

Multimodal photonic information systems are redefining the landscape of data storage and encryption by harnessing a diverse range of optical channels and stimuli-responsive materials. Within these interactive photonic platforms, information can be encoded not only through spatial patterns but also across wavelength (color), intensity, polarization, phase, and temporal domains, significantly enhancing storage density and security. This review provides a comprehensive overview of the current state of multimodal photonic encryption and data storage with particular emphasis on the chemistry of advanced luminescent and nanostructured materials and the device physics underlying their unique optical behaviors. We delve into fundamental photonic encoding strategies, ranging from structural color and metasurface holography to fluorescence/phosphorescence and electroluminescence, and discuss how multiple emission pathways can be coupled within single platforms. Various external stimuli (optical, electrical, magnetic, thermal, mechanical, and chemical) used to activate or unlock photonic signals are examined, highlighting designs in which specific stimuli act as secure cryptographic keys. We then review strategies for engineering multimodal photonic platforms, including the integration of orthogonal emission channels, spatial and temporal multiplexing, and device architectures (thin films, fibers, metasurfaces, and multilayers) that support complex hierarchical encoding. Mechanisms of photonic encryption are discussed, distinguishing static from dynamic approaches and detailing how stimuli-responsive modulation, time-gated luminescence lifetimes, and logical multistep unlocking schemes enhance security beyond conventional optical tags. Finally, application-oriented sections illustrate how these advances are being translated into real-world security architectures, ranging from anticounterfeiting labels, encrypted displays, and wearables to secure QR codes and optical memory elements for neuromorphic computing.

Noninvasive imaging through scattering media is crucial for diverse applications but remains constrained by a narrow field of view (FOV). Although recent learning-based methods have a larger FOV, they often require large-scale real experimental datasets and struggle when the FOV is far beyond the optical memory effect (OME). Here, we propose a physics-guided adaptive dual-domain diffusion model for ultra-wide-field noninvasive imaging through scattering media, namely UNI-Net. Specifically, we first develop a physical scattering imaging model to synthesize large-scale pre-training data, thereby reducing dependence on real experimental datasets. Second, to maximize the utilization of speckle information, we partition each speckle pattern into multi-channel patches to guide the diffusion process. Third, we propose a spatial-channel parallel attention block to model the spatial sparsity and inter-channel similarity of speckle patches with linear complexity. Extensive experiments show that our method cuts reliance on real experimental data by an order of magnitude and achieves a PSNR of 31.23 dB at a 41 OME range in complex scenes, which is 49.5% higher than existing approaches while requiring significantly lower computational and memory costs. Even at an extreme 164 OME range where other methods fail, it still reliably reconstructs complex scenes with a PSNR of 27.21dB.

Photogating and electrical pulse erasure in SnO2-based synaptic transistors for non-volatile optical memory applications

ABSTRACT Upconversion nanoparticle (UCNP)‐based hybrid heterostructures are emerging as a versatile platform for next‐generation optoelectronic devices and intelligent technologies, owing to their capability to convert low‐energy near‐infrared (NIR) photons into higher‐energy visible or ultraviolet (UV) emission. This review provides a critical overview of recent advances in UCNP design, interface engineering, and hybrid integration with organic, inorganic, and low‐dimensional materials, and clarifies how structural and compositional tailoring governs optical coupling and device performance. The diverse optoelectronic applications of UCNP hybrid systems, including photodetectors, solar cells, light‐emitting diodes (LEDs), and optical memories, are systematically discussed with particular emphasis on energy transfer and charge transport processes that underpin efficiency enhancement. Persistent challenges such as spectral mismatch, interfacial losses, and scalable fabrication are analyzed from both materials and device perspectives. Finally, future opportunities for UCNP hybrid heterostructures in intelligent sensing, neuromorphic information processing, biomedical applications, and space‐related optoelectronics are outlined, highlighting their potential to bridge photon conversion, information processing, and intelligent optoelectronic functionalities within unified material platforms.

Three phases of stoichiometric europium(III) iodate compounds are probed as candidates for optical quantum memory to determine the impact of reaction conditions on phase formation and to relate crystal structure to their ability to host persistent excited states relevant to quantum memory. Hydrothermal synthesis procedures for growing macroscopic single crystals of rectangular-block α-Eu(IO3)3 (P21/c), yellow hexagonal plate β-Eu(IO3)3 (P21/n), and transparent plate NaEu(IO3)4 (Cc) are presented. The ability to burn narrow and long-lived spectral holes on the 7F0 → 5D0 optical transitions is a necessary condition for implementing optical quantum memory. α-Eu(IO3)3 was determined to be incapable of hole-burning due to the presence of insufficient asymmetry of the europium site, which forbids the required optical transition. β-Eu(IO3)3 was determined to be incapable of storing information due to a lack of observed spectral hole burning, likely due to decoherence caused by the closely packed edge-sharing Eu(III) polyhedra. Analysis of each phase's crystal structure suggests that the Eu-Eu nearest neighbor distance and distortion of europium site symmetry are critical material design parameters for quantum memory applications.

ABSTRACT Reflective displays offer key advantages such as high energy efficiency, eye comfort, and excellent visibility, making them attractive for a wide range of applications. Recently, tunable photonics has focused on integrating electrochromic (EC) materials with optical resonant structures, enabling dynamic control of reflection spectra and intensity through enhanced light–matter interaction. Here, this review provides an overview of recent progress in electrochemically driven dynamic coloration devices, systematically categorized according to their tuning mechanisms and photonic design within the visible spectral range. We outline energy‐efficient and reconfigurable optical modulation strategies enabled by EC materials that operate through ionic insertion, redox reactions, or reversible plating/stripping of ionic metals. Furthermore, by bridging electrochemistry and photonic structures, we highlight recent progress, challenges, and emerging applications in reflective displays, colorimetric sensing, optical memory, and active holography. This review concludes by addressing the key challenges and future directions of this rapidly evolving field, highlighting the scientific and technological significance of electrochemical photonics as a foundation for next‐generation tunable optical systems.

Fast photochromism in Bi18SeO29:Ho3+ for dual-mode security and rewritable optical memory

Optical quantum memories enable long-distance quantum information distribution, which is critical for establishing the global quantum Internet. Rare-earth qubits are promising candidates due to their spin-optical interfaces with excellent spin coherence and optical properties. When doped into inorganic solids, they display relatively long excited-state lifetimes and, in turn, low storage efficiencies, which are difficult to optimize via material design. Metal–organic frameworks (MOFs) allow fine-tuning of spin coherence and excited-state lifetimes through rational design of coordination environments, thereby offering alternative platforms to host rare-earth qubits. By incorporating Nd3+ and Yb3+ into an oxalate-based MOF, we develop frameworks that exhibit spin decoherence time exceeding 5 μs at 3.4 K, near-infrared and/or telecommunication-band photoluminescence, and excited-state lifetimes up to 150 μs. These materials hold promise with long storage times and high storage efficiencies. Spin dynamics analysis reveals design principles to further improve coherence, promoting the development of rare-earth MOFs for quantum information science.

We present an inkjet-defined cholesteric liquid crystal (CLC) pixel architecture that enables frequency-addressable tristable optical memory for energy-efficient reflective modulation. Drop-on-demand printing forms individually programmable CLC droplets in a scalable, mask-free format, translating frequency-driven multistability into a manufacturable pixel platform. Each printed pixel can be electrically written into three optically distinct states: a transparent colored state, a colorless opaque concealment state, and a colored opaque memory state that remains stable after removal of the driving field. The tristability originates from frequency-selective competition between ion-mediated electrohydrodynamic convection at low frequency and dielectric realignment at high frequency. By encoding optical states through frequency programming rather than sustained bias, the device achieves true field-off retention without continuous power input. The printed arrays reach 63.5 dpi with uniform switching over areas up to 8.5 × 8.5 cm2, exhibit repeatable cycling without threshold drift, and show negligible thermal load during operation. By integrating structural coloration, transmittance modulation, and nonvolatile optical memory within a scalable inkjet process, this work establishes a practical pathway toward large-area smart windows, reflective displays, and programmable photonic surfaces.

Double perovskite films offer significant potential for multiferroic and ferroelectric photovoltaics due to their structural tunability. This study employs an aliovalent substitution strategy, partially replacing Bi with Pb in Bi2FeMnO6 (BFMO), to disrupt charge balance and local polarization while maintaining the host lattice. Pb incorporation simultaneously modulates the chemical states of all constituent elements, inducing pronounced lattice distortion and positive chemical strain. Unpoled Pb-BFMO films exhibit exceptional photovoltaic performance under 80 mW cm-2 illumination, achieving a short-circuit current density (|JSC|) of 192μAcm-2 and an open-circuit voltage (|VOC|) of 0.525V. This represents a 109-fold increase in intrinsic JSC and a fourfold enhancement in VOC compared to pure BFMO. The |JSC| demonstrates electric field tunability via polarization switching, reaching 320μAcm-2 under negative polarization, the highest reported JSC for sub-100nm single-layer ferroelectric films under white light. High-resolution high-angle annular dark-field scanning transmission electron microscopy, synchrotron-based reciprocal space mapping and X-ray absorption spectroscopy analyses collectively confirm the coupling of crystal distortion, chemical strain, and valence state alterations. The synergy between chemical strain and ionic valence states effectively engineers the bandgap and enhances photovoltaic response, which unlocks new application pathways for perovskite materials in optical memory devices and sustainable energy systems.

In the big-data-driven artificial intelligence era, similarity search, as a core operation in machine learning and data mining, demands high speed, energy efficiency, and scenario adaptability. Conventional electronic content-addressable memory (ECAMs) suffer from inherent RC delay bottlenecks, whereas existing optical content-addressable memory (OCAMs) are restricted by fixed bit-widths and limited distance metrics. In this work, we propose a variable bit-width all-optical CAM leveraging multi-segment modulators and phase-change material (PCM) Sb2Se3. The multi-segment memory unit (MSMU) therein compresses N-bit binary data into a single analog photonic unit, supporting direct data writing/loading without digital-to-analog converters (DACs) and flexible trade-offs between precision, storage capacity, noise immunity, and energy while enabling Hamming and nonlinear distance metrics. A six-element three-bit OCAM prototype was fabricated on a silicon nitride silicon-on-insulator (SiN-SOI) platform. Despite the absence of integrated high-speed phase shifters, the device still achieves reliable optical data storage and retrieval. K-nearest neighbor (kNN) simulations based on experimentally derived statistical data—validated on the iris, wine, and breast cancer datasets—show that the three-bit operating mode achieves classification accuracy comparable to Manhattan/Euclidean distances at high signal-to-noise ratios (SNRs), while the one-bit mode exhibits strong noise robustness. Energy consumption is 364 fJ/bit (3-bit) and 890 fJ/bit (1-bit). This work provides a high-speed, energy-efficient, and reconfigurable all-optical similarity search solution with experimentally verified device performance and dataset-validated applicability, showing great potential for widespread deployment in data-intensive machine learning and data-mining applications.

Advances in optoelectronic synapses (OES) have relied on complex device configurations and fabrication processes, which limit their practical implementation. Here, we exploit the untapped potential of ultrathin Bi2Te3 to construct a multifunctional OES device for a range of applications in neuromorphic computing, biometric recognition, and artificial visual perception. The Te vacancies in the film trap and de-trap charges, leading to persistent photoconductivity as the operating mechanism. Specifically, we demonstrate successful defect engineering by controlling the annealing temperature of the Bi2Te3 films and directly correlate the OES performance with the defect density. The role of the Te vacancies in OES is further confirmed by first-principles calculations. The OES devices show excellent metrics such as 191.7% paired-pulse facilitation and 37.2 fJ per spike of energy consumption. The device successfully simulates Pavlov's classic associative learning experiment. A 6 × 6 device array, serving as an artificial retina for image processing, displays excellent retention of the learned optical information and memory performance by 57.4%. The OES devices demonstrate high accuracy in facial recognition (93.3%) and urban traffic scene segmentation (86.7%) tasks after 100 epochs. Finally, successful optical logic gate operations and Morse code for optical signal recognition and wireless communication are demonstrated using the OES devices.

ABSTRACT The synergy between metasurfaces and non‐volatile phase change materials (PCMs) has created many reconfigurable photonic devices for applications in optical memory, optical computing, and optical communications. But these advances have been limited to the infrared wavelengths due to the high loss of PCMs in the visible regime. Here, we demonstrate a nonvolatile visible metasurface that is electrically reconfigurable using wide bandgap PCM Sb 2 S 3 . Our device supports a resonant mode at 610 nm, a wavelength largely under‐explored for PCM‐based metasurfaces. By incorporating only a 20 nm thick layer of Sb 2 S 3 , we experimentally demonstrate a resonance tuning range of 16 nm. Reversible switching of the metasurface is accomplished in situ using a carefully engineered, ultrathin doped silicon micro‐heater. Our work paves the way for integrating PCMs into visible‐frequency systems, particularly for human‐centric applications such as augmented and virtual reality displays.

Non-volatile optical memories represent a promising solution in data storage because of their high-speed, energy efficient, and durable data retention. Indeed, the interest of the research community in optical phase-change materials (O-PCMs) shows the urgent need for optical solutions in the non-volatile storage applications. Although several non-volatile optical memories using O-PCMs have been presented in the literature, their scalability is usually limited. In this article we show a non-volatile optoelectronic memory based on an electrically erasable programmable read-only grating (EEPROG) for the first time to our knowledge. A footprint per bit inversely proportional to the available bandwidth at the tunable source is demonstrated. Moreover, we show that to read the stored data in the EEPROG memory it is necessary only to sample the reflectivity spectrum of the EEPROG at a few wavelengths in a number equal to the number of bits stored in the memory. With 60 nm of available bandwidth at the tunable source (around 1500 nm), we demonstrate that one bit is stored in a footprint of 9.1 μm ×4.2 μm of the EEPROG memory. Higher available bandwidths would reduce the footprint per bit. The advantage with respect to the state of the art is the scalability of this memory, which represents one of the major bottlenecks of optical non-volatile memories. Indeed, the presented device allows a sequence of bits to be stored sequentially along the same waveguided structure, thus introducing a new level of scalability for optical non-volatile memories. The waveguiding device includes a Si 3 N 4 wire and a thin layer of Sb 2 S 3 as PCM, enabling non-volatile operations. The approach presented for storing and reading data is completely novel and could pave the way for a new series of non-volatile memories useful in several fields, including neuromorphic neural networks.

ABSTRACT Reliable, high‐density nonvolatile data storage requires new approaches to storage paradigms and readout mechanisms, underpinned by advanced multifunctional materials.​ Molecular ferroelectrics offer a promising pathway by leveraging reversible polarization switching and multidimensional optical properties. Herein, ultra‐smooth ferroelectric thin films of TMCM‐CdCl 3 were fabricated, with customizable 180° domains and ordered domain walls written via low voltage.​ Based on these designable ferroelectric domain patterns, diverse second‐harmonic generation (SHG) responses are observed at the engineered ferroelectric domain walls, and are tunable by the polarization of the incident light and the scanning depth. This phenomenon stems from the interplay between the nonlinear susceptibility tensors and axial polarization components in the diverse domain walls. Notably, we constructed the WS 2 /TMCM‐CdCl 3 van der Waals heterostructure and achieved multiple ferroelectric‐polarization‐dependent SHG modulation–effects absent in the isolated materials. Spectroscopy and theoretical analysis reveal that the SHG intensity reversal and polarization change arise from the interfacial coupling differences of the relative alignment of the ferroelectric polarization with the 2D material's polarity. Additionally, the system maintains near year‐long nonvolatile optical modulation and excellent environmental stability. These robust, multiple‐polarization‐dependent couplings and tunable behavior in nonlinear optics modulation could pave the way for nonvolatile optical memory and quantum photonic applications.

Optical neuromorphic computing utilizes light-based neural networks for efficient AI processing. Conventional optoelectronic synapses are limited by single-dimensional perception and electrical weight modulation. This study overcomes these constraints by developing all-photonic artificial synapses using water-mediated phase transitions in MAPbI₃ perovskite. These synapses exhibit reversible light-driven optical memory capabilities, achieving a broad transmittance modulation via precisely controlled crystal deformation mechanisms. The high-stability synapses successfully replicate neurobiological functions, including paired-pulse facilitation, short-term to long-term memory transition, and humidity-dependent plasticity. Implemented within a recurrent neural network, the synapses achieve 100% classification accuracy for multidimensional optical stimuli encompassing power, duration, and environmental humidity parameters. Furthermore, integration with a diffractive deep neural network enables reconfigurable computing with 80 distinct programmable transmittance states, achieving remarkable classification accuracies on the MNIST handwritten digits and Fashion-MNIST datasets without hardware modifications. This work establishes a paradigm for developing intelligent systems that adapt to complex environmental changes, demonstrating potential for applications in dynamic visual perception and multi-task processing environments. Optical neuromorphic computing faces challenges with single-dimensional perception and electrical weight modulation in traditional synapses. Here, the authors develop all-photonic artificial synapses using MAPbI₃ perovskites, achieving reversible optical memory and high classification accuracy, paving the way for adaptive intelligent systems in dynamic environments and enhancing multi-task processing capabilities.

Abstract In the development of high-performance optical quantum memories, Er 3+ :CaWO 4 has emerged as a compelling rare-earth ion-doped solid-state platform owing to its long optical coherence times and compatibility with the 1550 nm telecommunications band. In this work, we systematically investigate how optical polarisation and temperature influence the absorption strength, central wavelength, and linewidth of the Z 1 → Y 1 and Z 1 → Y 2 transitions, with light incident along the crystal a and c axes. We identify the Z 1 → Y 1 transition at 1532.6 nm with the incident laser propagating along the c -axis at cryogenic temperatures (~3 K) as particularly advantageous for memory operation. Under these conditions, the transition exhibits a stable central wavelength, narrower linewidth, polarisation-independent absorption, and an enhanced absorption cross-section, while residing within the telecommunication C-band. These combined attributes position the Z 1 → Y 1 line as a robust and suitable point for Er 3+ :CaWO 4 based quantum memories.

Neuromorphic computing—modeled after the functionality and efficiency of biological neural systems—offers promising new directions for advancing artificial intelligence and computational models. Photonic techniques for neuromorphic computing hardware are attracting increasing research interest, thanks to their potentials for ultra high bandwidths, low crosstalk, and high parallelism. Among these, approaches based upon resonant tunneling diodes (RTDs) have recently gained attention as potential building blocks for next‐generation light‐enabled neuromorphic hardware, due to their capacity to replicate key neuronal behaviors such as excitable spiking and refractoriness, added to their potentials for high operational speeds, energy efficiency and compact footprints. In particular, their ability to function as opto‐electronic spiking neurons makes them strong candidates for integration into novel event‐based neuromorphic computing systems. This work demonstrates the application of optically triggered spiking RTD neurons to a multiplicity of applications and architectures; these include systems based upon single elements for multimodal (photonic‐electronic) fast rising edge‐detection in time‐series data, the construction of a two‐layer feedforward artificial photonic spiking neural network (pSNN) using RTD neurons as the nonlinear nodes delivering excellent performance in complex dataset classification tasks, and a pSNN comprised of multiple coupled light‐sensitive RTD spiking neurons that supports performance as an adjustable neuromorphic optical spiking memory system with a tunable storage time of spiking patterns.

Photon echo (PE) techniques offer a promising approach to optical quantum memory, yet their implementation in conventional platforms, such as rare-earth-ion-doped crystals, is hindered by limited bandwidths. Semiconductor quantum dot (QD) ensembles, featuring THz-scale inhomogeneous broadening and sub-picosecond dynamics, provide an attractive alternative for ultrafast applications. However, achieving coherent control across such broad spectral ranges remains challenging due to detuning and spatial field inhomogeneities, which reduce PE efficiency. In this work, we demonstrate that chirped rephasing pulses satisfying adiabatic conditions enable robust adiabatic rapid passage (ARP) across an inhomogeneously broadened InAs QD ensemble. This approach achieves uniform population inversion and broadband rephasing, overcoming the limitations of transform-limited excitation. Experimentally, we observe a 3.2-fold enhancement of the PE signal in dense, self-assembled InAs QDs operating at telecom wavelengths. Numerical simulations based on a two-level model reproduce the experimentally observed ARP-induced enhancement, validating the underlying physical mechanism. These results establish ARP as an effective and scalable method for coherent control in THz-broadened QD ensembles, opening a pathway toward ultrafast and broadband optical quantum memory and communication in the telecom band.