Search Results (206)

Chiral bound states in the continuum (BIC) metasurfaces have emerged as a promising platform for enhancing light–matter interactions, which have potential applications in advanced photonic and quantum information devices. However, simultaneously achieving near-perfect circular dichroism and highly efficient nonlinear conversion with highly symmetric structures in metasurfaces remains an open challenge. In this work, we design a $C_4$-symmetric chiral metasurface composed of eight elliptical silicon nanorods on a ${\mathrm{SiO}}_2$ substrate, where monocrystalline silicon is used as the nonlinear optical material. By combining simulations and nonlinear time-domain coupled-mode theory (TCMT), we discovered that both the optimal chirality and the nonlinear conversion efficiency can be attained simultaneously due to the critical coupling between the metasurface mode and the quasi-BIC mode. Meanwhile, a near-perfect circular dichroism (CD = 0.99) and a high nonlinear conversion efficiency of $7\times {10}^{-5}$ under a radiation intensity of $5{\mathrm{kW}/\mathrm{cm}}^2$ are numerically achieved due to the robustness of bound states in the continuum. This work offers a promising route toward high-performance chiral nonlinear photonic components, which is of great importance for the development of ultra-compact optical devices such as circular polarization detectors, chiral sensors, and nonlinear photonic chips for integrated optical and quantum information systems. Our research not only contributes to the fundamental understanding of chiral metasurfaces but also provides a practical approach for achieving high-efficiency nonlinear optical devices. Full article

This paper presents the design, modeling, and numerical validation of a compact artificial magnetic conductor (AMC)–backed flexible UHF RFID tag antenna intended for on-body biomedical and wearable sensing applications. Human tissue proximity typically causes severe detuning, radiation efficiency degradation, and increased specific absorption rate (SAR) for conventional RFID tag antennas. To address these limitations, a miniaturized AMC metasurface based on a modified Jerusalem-cross geometry with meandered and interdigitated features is developed on a high-permittivity biocompatible substrate using CST Studio Software (2025). Full-wave simulations demonstrate that the proposed design, with an ultra-compact footprint of 0.0246 λ² (32.12 mm × 64.24 mm), functions as an effective shielding element, significantly enhancing the tag antenna gain and reading range by an order of magnitude compared to conventional on-body tags, while simultaneously reducing backward radiation and SAR. The antenna demonstrates robust platform tolerance and excellent isolation from the human body, ensuring high reliability. Fabricated on a thin, flexible, biocompatible, silicon-doped dielectric substrate, this device also functions as an epidermal antenna for on-skin health parameter sampling. This research paves the way for advanced, non-invasive wearable medical devices with superior performance. Full article

Arrhythmia is a life-threatening cardiovascular disease if not detected early. While deep learning models have demonstrated strong performance in ECG-based arrhythmia classification, deploying these models on resource-constrained wearable devices remains challenging. In this paper, we present a quantization-compatible neural architecture search (NAS) framework that discovers ultra-compact minority-aware convolutional neural networks (CNN). We formulate NAS as a multi-objective optimization problem, jointly maximizing balanced accuracy and minority-classes recall while minimizing model size and computational complexity. Furthermore, we constrain our search space to INT8-compatible operations. We evaluated our framework on the MIT-BIH Arrhythmia Database. We discovered NanoCNN models for binary and multi-class classification tasks. The models trained without augmentation achieved 98.7% and 98.21% overall accuracies outperforming the state-of-the-art. The discovered models required 38.3 K and 51.5 K multiply-accumulate operations (MAC) per inference, enabling their deployment on ARM Cortex-M4 microcontrollers. With augmentation and other minority-aware interventions, our model attained 91.6% balanced accuracy. Our results validated the effectiveness of the adopted search and training techniques for arrhythmia screening and diagnosis. Full article

Although the polarization state, as a key physical dimension of light, plays an irreplaceable role in many frontier fields such as quantum communication and chiral sensing, traditional photodetectors are limited by the inherent optical isotropy of materials and thus are unable to directly distinguish circular polarization information. This paper numerically reports a miniature circular polarization photodetector based on chiral metasurfaces, which achieves an excellent extinction ratio of up to 31 dB through the collaborative regulation of geometric displacement manipulation and tilt angle operation. This device utilizes the symmetry-breaking effect to construct significantly different transmission spectral responses between left circularly polarized light (LCP) and right circularly polarized light (RCP). Our research not only provides a high-performance implementation solution for on-chip polarization detection but also opens up new paths for the future development of quantum optics, integrated sensing, and ultra-compact polarization optical systems. Full article

To address the dual demands of accuracy and real-time performance in power quality disturbance (PQD) recognition for new power system, this paper proposes a lightweight model named the Cross-Channel Attention Three-Layer Convolutional Model (1D-CCANet-3), specifically designed for edge deployment. Based on the one-dimensional convolutional neural network (1D-CNN), the model features an ultra-compact architecture with only three convolutional layers and one fully connected layer. By incorporating a set of cross-channel attention (CCA) mechanisms in the final convolutional layer, the model further enhances disturbance recognition accuracy. Compared to other deep learning models, 1D-CCANet-3 significantly reduces computational and storage requirements for edge devices while achieving accurate and efficient PQD recognition. The model demonstrates robust performance in recognizing 10 types of PQD under varying signal-to-noise ratio (SNR) conditions. Furthermore, the model has been successfully deployed on the FPGA platform and exhibits high recognition accuracy and efficiency in real-world data validation. This work provides a feasible and effective solution for accurate and real-time PQD monitoring on edge devices in new power systems. Full article

Seed phenomics is a critical research field for understanding seed germination mechanisms. Metasurfaces, composed of subwavelength nanostructures, offer a promising pathway to achieve both dispersion control and imaging functionalities within an ultra-compact form factor. Recent advances in micro–nano-optics and computational imaging have opened new avenues for high-dimensional, multimodal imaging. However, conventional hyperspectral and light-field systems still face limitations in compactness, depth resolution, and spectral–spatial integration. This review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction, with a focus on the underlying principles, design strategies, and reconstruction algorithms that enable single-shot 3D hyperspectral acquisition. We further present a forward-looking roadmap toward the realization of a revolutionized imaging paradigm: a metasurface-based light-field platform that fully integrates 3D and hyperspectral imaging capabilities. In particular, we examine how dispersive metasurfaces serve as core optical elements for precise dispersion control in hyperspectral imaging systems, while metalens arrays enable accurate modulation of spatial–angular distributions in light-field configurations. We systematically review both 3D and spectral reconstruction algorithms, highlighting their roles in decoding complex optical encodings. The application of these integrated systems in seed phenotyping is emphasized, demonstrating their capability to capture 3D spatial–spectral distributions in a single exposure. This approach facilitates high-throughput analysis of morphological traits, germination potential, and internal biochemical composition, offering a comprehensive solution for advanced seed characterization. Finally, we outline a practical roadmap for implementing a metasurface-based light-field platform that integrates hyperspectral imaging and computational 3D reconstruction. This review offers a comprehensive overview of the state of the art in compact 3D light-field systems and multimodal hyperspectral imaging platforms, while providing forward-looking insights aimed at advancing smart seed phenotyping, precision agriculture, and next-generation optical imaging technologies. Full article

Thin-film lithium niobate (TFLN) electro-optic modulators (EOMs) offer distinct advantages, including high speed, broad bandwidth, and low power consumption. However, their large size hinders the density of integration, which trades off with the half-wave voltage. Photonic crystal (PC) structures can effectively reduce the device footprint via the slow-light effect; however, they experience significant losses due to fabrication defects and sharp corners. Here, we theoretically demonstrate an ultracompact Mach–Zehnder interferometer (MZI) EOM based on a TFLN valley photonic crystal (VPC) structure. The design can achieve a high forward transmittance (>0.8) due to defect-immune unidirectional propagation in the VPC, enabled by the unique spin-valley locking effect. The EOM, with a small footprint of 21 μm × 17 μm, achieves an extinction ratio of 16.13 dB and a modulation depth of 80%. The design can be experimentally fabricated using current nanofabrication techniques, making it suitable for broad applications in optical communications. Full article

This work presents a highly compact and scalable 1.2-kV SiC MOSFET half-bridge building-block module enabled by a die-integrated 3D PCB packaging technology. Compared with conventional DBC-based or TO-247-based SiC half-bridge modules, the proposed design reduces the physical volume and weight by more than 90% while maintaining full compatibility with standard PCB manufacturing processes. The vertically laminated DC+/DC− conductors and symmetric PCB–die–PCB stack establish a tightly confined commutation loop, resulting in a measured power-loop inductance of 2.2 nH and a 3.8 nH gate-loop inductance—representing up to 94% and 89% reduction relative to discrete device implementations. Because the parasitic parameters are intrinsically well-balanced across replicated units and the mutual inductance between adjacent modules remains extremely small, the structure naturally supports current sharing during parallel operation. Thermal and insulation evaluations further confirm the suitability of copper filling via high-Tg laminated PCB substrates for high-power SiC applications, achieving withstand voltages exceeding twice the rated bus voltage. The proposed module is experimentally validated through finite-element parasitic extraction and 950 V double-pulse testing, demonstrating controlled dv/dt behavior and robust switching performance. This work establishes a manufacturable and parallel-friendly packaging approach for high-density SiC power conversion systems. Full article

The James Webb Space Telescope (JWST) has begun to revolutionize our view of the Cosmos. The discovery of Blue Monsters (i.e., ultra-compact yet very bright high-z galaxies) and the Little Red Dots (i.e., very compact dustless strong Balmer break cosmic dawn sources) pose significant challenges to pre-JWST era models of the assembly of first stars and galaxies. In addition, JWST data further strengthen the problem posed by the origin of the supermassive black holes that power the most distant quasars observed. Stars powered by Dark Matter annihilation (i.e., Dark Stars) can form out of primordial gas clouds during the cosmic dawn era and subsequently might grow via accretion and become supermassive. In this paper we argue that Supermassive Dark Stars (SMDSs) offer natural solutions to the three puzzles mentioned above. Full article

We introduce an ultra-compact, single-neuron-per-class end-to-end readout for binary classification of noisy, image-encoded sensor time series. The approach compares a linear single-unit perceptron (E2E-MLP-1) with a resonate-and-fire (RAF) neuron (E2E-RAF-1), which merges feature selection and decision-making in a single block. Beyond empirical evaluation, we provide a mathematical analysis of the RAF readout: starting from its subthreshold ordinary differential equation, we derive the transfer function $H\left(\mathrm{j}\omega \right)$, characterize the frequency response, and relate the output signal-to-noise ratio (SNR) to ${\left|H\left(\mathrm{j}\omega \right)\right|}^2$ and the noise power spectral density $S_n\left(\omega \right)\propto {\omega }^{\alpha }$ (brown, pink, and blue noise). We present a stable discrete-time implementation compatible with surrogate gradient training and discuss the associated stability constraints. As a case study, we classify walk-in-place (WIP) in a virtual reality (VR) environment, a vision-based motion encoding (72 × 56 grayscale) derived from 3D trajectories, comprising 44,084 samples from 15 participants. On clean data, both single-neuron-per-class models approach ceiling accuracy. At the same time, under colored noise, the RAF readout yields consistent gains (typically +5–8% absolute accuracy at medium/high perturbations), indicative of intrinsic band-selective filtering induced by resonance. With ∼8 k parameters and sub-2 ms inference on commodity graphical processing units (GPUs), the RAF readout provides a mathematically grounded, robust, and efficient alternative for stochastic signal processing across domains, with virtual reality locomotion used here as an illustrative validation. Full article

Miniaturized long-wavelength infrared (LWIR) imaging systems are highly desirable for applications such as portable thermal sensing, unmanned surveillance, and medical diagnostics. Conventional refractive optics in the LWIR regime often require multiple lens configurations to extend depth of field (DoF), leading to increased size, weight, and cost. Although existing LWIR metalenses demonstrate competent capabilities, comprehensive approaches to DoF engineering have yet to be explored. Here, we demonstrate a miniature large-DoF camera using a metalens. The designed metalens features a 14 mm diameter aperture and weighs only 0.8 g while maintaining sharp focus over a working distance ranging from 1 m to 22 m. By leveraging subwavelength phase engineering, the metalens achieves high-resolution imaging with low aberration. The integrated camera exhibits an ultra-compact form factor, i.e., 2.3 cm × 2.3 cm × 1.2 cm (length × width × height) and weighs just 25 g. Experimental results confirm the superior DoF performance, enabling clear imaging across varying distances without mechanical refocusing. The advance provides a promising pathway toward ultra-compact, large-DoF LWIR imaging systems for applications ranging from autonomous vehicles to portable medical diagnostics and miniature surveillance devices. Full article

With the increasing demand for ultrafast optical signal processing, silicon-on-silica (SoS) waveguides with ring resonators have emerged as a promising platform for integrated all-optical logic gates (AOLGs). In this work, we design and simulate a SoS-based waveguide structure, operating at the telecommunication wavelength of 1550 nm, consisting of a circular ring resonator coupled to straight bus waveguides using Lumerical FDTD solutions. The design achieves a high Q-factor of 11,071, indicating low optical loss and strong light confinement. The evanescent coupling between the ring and waveguides, along with optimized waveguide dimensions, enables efficient interference, realizing a complete suite of AOLGs (XOR, AND, OR, NOT, NOR, NAND, and XNOR). Numerical simulations demonstrate robust performance across all gates, with high contrast ratios between 11.40 dB and 13.72 dB and an ultra-compact footprint of 1.42 × 1.08 µm². The results confirm the device’s capability to manipulate optical signals at data rates up to 55 Gb/s, highlighting its potential for scalable, high-speed, and energy-efficient optical computing. These findings provide a solid foundation for the future experimental implementation and integration of SoS-based photonic logic circuits in next-generation optical communication systems. Full article

A general relativistic model of an astrophysical hypermassive extremely magnetized ultra-compact self-bound quark–gluon plasma (QGP: ALICE/LHC) object that is supported against its ultimate gravitational implosion by the simultaneous action of the vacuum polarization driven by nonlinear electrodynamics (NLED: ATLAS/LHC: light-by-light scattering)—the vacuum “awakening”—and the asymptotic freedom, a key feature of quantum chromodynamics (QCD), is presented. These QCD stars can be the final figures of the equilibrium of collapsing stellar cores permeated by magnetic fields with strengths well beyond the Schwinger threshold due to being self-bound, and for which post-supernova fallback material pushes the nascent remnant beyond its stability, forcing it to collapse into a hybrid hypermassive neutron star (HHMNS). Hypercritical accretion can drive its innermost core to spontaneously break away color confinement, powering a first-order hadron-to-quark phase transition to a sea of ever-freer quarks and gluons. This core is hydro-stabilized by the steady, endlessly compression-admitting asymptotic freedom state, possibly via gluon-mediated enduring exchange of color charge among bound states, e.g., the odderon: a glueball state of three gluons, or either quark-pairing (color superconductivity) or tetraquark/pentaquark states (LHCb Coll.). This fast—at the QGP speed of sound—but incremental quark–gluon deconfinement unbinds the HHMNS’s baryons so catastrophically that transforms it, turning it inside-out, into a neat self-bound QGP star. A solution to the nonlinear Tolman–Oppenheimer–Volkoff (TOV) equation is obtained—that clarifies the nonlinear effects of both NLED and QCD on the compact object’s structure—which clearly indicates the occurrence of hypermassive QGP/QCD stars with a wide mass spectrum ($0\lesssim {\mathrm{M}}_{\mathrm{Star}}^{\mathrm{QGP}}\lesssim$ 7 M_⊙ and beyond), for star radii ($0\lesssim R_{\mathrm{Star}}^{\mathrm{QGP}}\lesssim 24$ km and beyond) with B-fields (${10}^{14}\le {\mathrm{B}}_{\mathrm{Star}}^{\mathrm{QGP}}\le {10}^{16}$ G and beyond). This unexpected feature is described by a novel mass vs. radius relation derived within this scenario. Hence, endowed with these physical and astrophysical characteristics, such QCD stars can definitively emulate what the true (theoretical) black holes are supposed to gravitationally do in most astrophysical settings. This color quark star could be found through a search for its eternal “yo-yo” state gravitational-wave emission, or via lensing phenomena like a gravitational rainbow (quantum mechanics and gravity interaction), as in this scenario, it is expected that the light deflection angle—directly influenced by the larger effective mass/radius (${\mathrm{M}}_{\mathrm{Star}}^{\mathrm{QGP}}\left(B\right)$, ${\mathrm{R}}_{\mathrm{Star}}^{\mathrm{QGP}}\left(B\right)$) and magnetic field of the deflecting object—increases as the incidence angle decreases, in view of the lower values of the impact parameter. The gigantic—but not infinite—surface gravitational redshift, due to NLED photon acceleration, makes the object appear dark. Full article

This paper presents a novel methodology for the design and calibration of ultra-compact digital-to-analog converters (DACs), integrating architectural redundancy and a digital calibration algorithm. The proposed calibration approach generates pre-distortion codes that correct both positive and negative nonlinearity errors, even in designs with severe mismatch or relaxed layout constraints. This enables the use of aggressively scaled devices while maintaining high linearity and spectral fidelity. The algorithm is architecture-agnostic and compatible with resistor-string, current-steering, and hybrid DAC structures. It operates with minimal memory, low latency, and supports both foreground and background calibration modes. The method is validated through simulation and silicon measurement of three 14-bit DAC architectures fabricated in TSMC 180 nm CMOS. Post-calibration results demonstrate linearity within ±0.5–1.2 LSB, ENOB up to 13.8 bits, and significant improvements in SNR, SFDR, and THD. The compact layouts—occupying as little as 0.0169 mm²—highlight the scalability of the proposed method for applications such as analog AI accelerators and high-density mixed-signal SoCs. Full article

Rice, a dietary staple for over half of the global population, is highly susceptible to bacterial and fungal diseases such as bacterial blight, brown spot, and leaf smut, which can severely reduce yields. Traditional manual detection is labor-intensive and often results in delayed intervention and excessive chemical use. Although deep learning models like convolutional neural networks (CNNs) achieve high accuracy, their computational demands hinder deployment in resource-limited agricultural settings. We propose MCP-X, an ultra-compact CNN with only 0.21 million parameters for real-time, on-device rice disease classification. MCP-X integrates a shallow encoder, multi-branch expert routing, a bi-level recurrent simulation encoder–decoder (BRSE), an efficient channel attention (ECA) module, and a lightweight classifier. Trained from scratch, MCP-X achieves 98.93% accuracy on PlantVillage and 96.59% on the Rice Disease Detection Dataset, without external pretraining. Mechanistically, expert routing diversifies feature branches, ECA enhances channel-wise signal relevance, and BRSE captures lesion-scale and texture cues—yielding complementary, stage-wise gains confirmed through ablation studies. Despite slightly higher FLOPs than MobileNetV2, MCP-X prioritizes a minimal memory footprint (~1.01 MB) and deployability over raw speed, running at 53.83 FPS (2.42 GFLOPs) on an RTX A5000. It achieves 16.7×, 287×, 420×, and 659× fewer parameters than MobileNetV2, ResNet152V2, ViT-Base, and VGG-16, respectively. When integrated into a multi-resolution ensemble, MCP-X attains 99.85% accuracy, demonstrating exceptional robustness across controlled and field datasets while maintaining efficiency for real-world agricultural applications. Full article