Search Results (1,648)

Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C₃H₁₂Si₃), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650–800 °C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of ~0.5 Å/s and minimal surface roughness of ~5 Å. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (~2.7) and a minimal absorption coefficient (<200 cm⁻¹) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies. Full article

Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial. Full article

Optical materials are widely used in large optical systems such as lithography machines and astronomical telescopes. However, optical materials inevitably produce subsurface damage (SSD) during lapping and polishing processes, degrading the laser damage threshold and impacting the service life of the optical system. The large surface roughness of the lapped optical materials further increases the difficulty of the nondestructive detection of SSD. Quantum dots (QDs) show great development potential in the nondestructive detection of SSD in lapped materials. However, existing QD-based SSD detection methods ignore the polarization sensitivity of QDs to excitation light, which affects the detection accuracy of SSD. To address this problem, this paper explores the fluorescence polarization properties of QDs in the SSD of optical materials. First, the detection principle of SSD based on the fluorescence polarization of QDs is investigated. Subsequently, a fluorescence polarization detection system is developed to analyze the fluorescence polarization properties of QDs in SSD. Finally, the SSD is detected based on the studied polarization properties. The results show that the proposed method effectively improves the detection rate of SSD by 10.8% and thus provides guidance for evaluating the quality of optical material and optimizing optical material processing technologies. The research paradigm is equally applicable to biomedicine, energy, optoelectronics, and the environment, where QDs have a wide range of applications. Full article

Levitation techniques reduce the available heterogeneous nucleation sites and provide stable access to deeply undercooled melts. However, some samples have repeatably demonstrated that, in the presence of strong stirring, solidification may be induced at moderate, sub-critical undercoolings. Dynamic nucleation is a mechanism by which solidification may be induced through flow effects within a sub-critically undercooled melt. In this mechanism, collapsing cavities within the melt produce very high-pressure shocks, which shift the local melting temperature. In these regions of locally shifted melt temperatures, thermodynamic conditions enable nuclei to grow and trigger solidification of the full sample. By deepening the local undercooling, dynamic nucleation enables solidification to occur in conditions where classical nucleation does not. Dynamic nucleation has been observed in several zirconium and zirconium-based samples in the Electromagnetic Levitator onboard the International Space Station (ISS-EML). The experiments presented here address conditions in which a zirconium sample alloyed with 2.5 atomic percent niobium spontaneously solidifies during electromagnetic levitation experiments with strong melt stirring. In these experimental conditions, classical nucleation predicts the sample to remain liquid. This solidification behavior is consistent with the solidification behavior observed in prior experiments on pure zirconium. Full article

Nitrogen vacancy (NV) diamonds are promising room temperature quantum sensors. As the technology moves towards application, efficient use of energy and cost become critical for miniaturization. This work focuses on microwave-based spin control using the short-circuited end of a slot line, analyzed by finite element method (FEM) for magnetic field amplitude and uniformity. A microstrip-to-slot-line converter with a $-10$ dB bandwidth of $3.2$ GHz was implemented. Rabi oscillation measurements with an NV microdiamond on a glass fiber show uniform excitation over $1.5$ MHz across the slot, allowing spin manipulation within the coherence time of the NV center. Full article

Quantum sensing leverages quantum resources to enable ultra-precise measurements beyond classical limits, driving transformative advancements in metrology. Optical fiber quantum sensing, integrating optical fiber sensing with quantum technologies, enhances measurement precision and sensitivity from multiple perspectives, such as exploring high-sensitivity optical fiber sensing installations and generating high-quality optical fiber quantum states. Following decades of comprehensive investigations and remarkable advances in optical fiber quantum sensing technology, this review systematically examines research achievements in this field through two complementary perspectives: one is the basic principle of generating optical fiber quantum states and their applications in sensing and the other is optical fiber quantum interferometers and their applications in sensing. Finally, examine current opportunities and challenges as well as the future development of optical fiber quantum sensing. Full article

This study focuses on the analysis of soliton solutions within the framework of the time-fractional cubic–quintic nonlinear Schrödinger equation (TFCQ-NLSE), a powerful model with broad applications in complex physical phenomena such as fiber optic communications, nonlinear optics, optical signal processing, and laser–tissue interactions in medical science. The nonlinear effects exhibited by the model—such as self-focusing, self-phase modulation, and wave mixing—are influenced by the combined impact of the cubic and quintic nonlinear terms. To explore the dynamics of this model, we apply a robust analytical technique known as the sub-ODE method, which reveals a diverse range of soliton structures and offers deep insight into laser pulse interactions. The investigation yields a rich set of explicit soliton solutions, including hyperbolic, rational, singular, bright, Jacobian elliptic, Weierstrass elliptic, and periodic solutions. These waveforms have significant real-world relevance: bright solitons are employed in fiber optic communications for distortion-free long-distance data transmission, while both bright and dark solitons are used in nonlinear optics to study light behavior in media with intensity-dependent refractive indices. Solitons also contribute to advancements in quantum technologies, precision measurement, and fiber laser systems, where hyperbolic and periodic solitons facilitate stable, high-intensity pulse generation. Additionally, in nonlinear acoustics, solitons describe wave propagation in media where amplitude influences wave speed. Overall, this work highlights the theoretical depth and practical utility of soliton dynamics in fractional nonlinear systems. Full article

We present a theoretical study of various designs for arbitrary polarization retarders, created using sequences of half-wave and quarter-wave plates arranged at specific rotation angles. When combined with arbitrary polarization rotators, these retarders form a flexible device capable of implementing transformations between any pair of polarization states. Some configurations discussed are known from existing literature, while others appear to be new and, to the best of our knowledge, have not been reported before. The devices allow for continuous tuning of both retardance and rotation by adjusting the relative angles between the wave plates in the sequence. Full article

With the development of a quantum computer in the near future, classical public-key cryptography will face the challenge of being vulnerable to quantum algorithms, such as Shor’s algorithm. As communication technology advances rapidly, a great deal of personal information is being transmitted over the Internet. Based on our observation that the Kyber algorithm exhibits a significant number of idle cycles during execution when implemented following the conventional software procedure, this paper proposes a high-throughput scheduling for Kyber by parallelizing the SHA-3 function, the sampling algorithm, and the NTT computations to improve hardware utilization and reduce latency. We also introduce the 8-stage pipelined SHA-3 architecture and multi-mode polynomial arithmetic module to increase area efficiency. By also optimizing the hardware architecture of the various computational modules used by Kyber, according to the implementation result, an aggregate throughput of 877.192 kOPS in Kyber KEM can be achieved on TSMC 40 nm. In addition, our design not only achieves the highest throughput among existing studies but also improves the area and power efficiencies. Full article

In this work, we present a high-performance tapered quantum cascade laser (QCL) designed to achieve both high output power and low divergence angle. By integrating a tapered waveguide with a Fabry–Perot structure, significant improvements of tapered QCL devices in both output power and beam quality are demonstrated. The optimized 50 µm wide tapered QCL achieved a maximum output power of 2.76 W in pulsed operation with a slope efficiency of 3.52 W/A and a wall-plug efficiency (WPE) of 16.2%, while reducing the divergence angle to 13.01°. The device maintained a maximum power of 1.34 W with a WPE exceeding 8.2%, measured under room temperature and continuous wave (CW) operation. Compared to non-tapered Fabry–Perot QCLs, the tapered devices exhibited a nearly 10-fold increase in output power and over 200% improvement in WPE. This work provides a promising pathway for advancing mid-infrared laser technology, particularly for applications requiring high power, low divergence, and temperature stability. Full article

Interest in germanium electronic devices is experiencing a comeback thanks to their suitability for a wide range of new applications, like CMOS transistors, quantum technology or infrared photonics. Among these applications, Ge-based thermophotovoltaic converters could become the backbone of thermo-electrical batteries. However, these devices are still far from the efficiency threshold needed for industrial deployment, with surface recombination as the main limiting factor for the material. In this work, we discuss the main passivation techniques developed for germanium photovoltaic and thermophotovoltaic devices, summarizing their main advantages and disadvantages. The analysis reveals that surface recombination velocities as low as 2.7 cm/s and 1.3 cm/s have already been reported for p-type and n-type germanium, respectively, although improving surface recombination velocities below 100 cm/s would result in marginal efficiency gains. Therefore, the main challenge for the material is not reducing this parameter further but developing robust and reliable processes for integrating the current techniques into functional devices. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

An altermagnet, characterized by its distinctive magnetic properties, may hold potential applications in diverse fields such as magnetic materials, spintronics, data storage, and quantum computing. As a prototypical altermagnet, RuO₂ exhibits spin polarization and demonstrates the advantageous characteristics of high electrical conductivity and low thermal conductivity. These exceptional properties endow it with considerable promise in the emerging field of thermal spintronics. We studied the electronic structure and thermoelectric properties of RuO₂; the constructed RuO₂/TiO₂/RuO₂ all-antiferromagnetic tunnel junction (AFMTJ) exhibited thermally induced magnetoresistance (TIMR), reaching a maximum TIMR of 1756% at a temperature gradient of 5 K. Compared with prior studies on RuO₂-based antiferromagnetic tunnel junctions, the novelty of this work lies in the thermally induced magnetoresistance based on its superior thermoelectric properties. In parallel structures, the spin-down current dominates the transmission spectrum, whereas in antiparallel structures, the spin-up current governs the transmission spectrum, underscoring the spin-polarized thermal transport. In addition, thermoelectric efficiency emphasizes the potential of RuO₂ to link antiferromagnetic robustness with ferromagnetic spin functionality. These findings promote the development of efficient spintronic devices and spin-based storage technology for waste heat recovery and emphasize the role of spin splitting in zero-magnetization systems. Full article

The quantum relative entropy between a quantum state and its Gaussian equivalent is a quantifying function of the system’s non-Gaussianity, a useful resource in several applications, such as quantum communication and computation. One of its most fundamental properties is to be monotonically decreasing under Gaussian evolutions. In this paper, we develop the conditions for a non-Gaussian quantum channel to preserve the monotonically decreasing property. We propose a necessary condition to classify between Gaussian and non-Gaussian channels and use it to define a class of quantum channels that decrease the system’s non-Gaussianity. We also discuss how this property, combined with a restriction on the states at the channel’s input, can be applied to the security analysis of continuous-variable quantum key distribution protocols. Full article