Ultrafast Speed Research Articles

Creating Anti-Chiral Exceptional Points in Non-Hermitian Metasurfaces for Efficient Terahertz Switching.

Non-Hermitian degeneracies, also known as exceptional points (EPs), have presented remarkable singular characteristics such as the degeneracy of eigenvalues and eigenstates and enable limitless opportunities for achieving fascinating phenomena in EP photonic systems. Here, the general theoretical framework and experimental verification of a non-Hermitian metasurface that holds a pair of anti-chiral EPs are proposed as a novel approach for efficient terahertz (THz) switching. First, based on the Pancharatnam-Berry (PB) phase and unitary transformation, it is discovered that the coupling variation of ±1 spin eigenstates will lead to asymmetric modulation in two orthogonal linear polarizations (LP). Through loss-induced merging of a pair of anti-chiral EPs, the decoupling of ±1 spin eigenstates are then successfully realized in a non-Hermitian metasurface. Final, the efficient THz modulation is experimentally demonstrated, which exhibits modulation depth exceeding 70% and Off-On-Off switching cycle less than 9ps in one LP while remains unaffected in another one. Compared with conventional THz modulation devices, the metadevice shows several figures of merits, such as a single frequency operation, high modulation depth, and ultrafast switching speed. The proposed theory and loss-induced non-Hermitian device are general and can be extended to numerous photonic systems varying from microwave, THz, infrared, to visible light.

Hybrid synaptic structure for spiking neural network realization

Neural networks and neuromorphic computing represent fundamental paradigms as alternative approaches to Von-Neumann-based implementations, advancing in the applications of deep learning and machine vision. Nonetheless, conventional semiconductor circuits encounter challenges in achieving ultra-fast processing speed and low power consumption due to their dissipative properties. Conversely, single flux quantum circuits exhibit inherent spiking behavior, showcasing their characteristics as a promising candidate for spiking neural networks (SNNs). In this work, we present a compact hybrid synapse circuit to mimic the biological interconnect functionality, enabling the weighting operations for excitatory and inhibitory impulses. Additionally, the proposed structure facilitates input accumulation, which is performed before the activation function. In the experiments, our synaptic structure interfaces with a soma circuit fabricated using a commercial Nb process, underscoring its compatibility and supporting its potential for integration into efficient neural network architectures. The weight value on the synapse is configurable by utilizing cryo-CMOS circuits, providing adaptability to the inference networks. We’ve successfully designed, fabricated, and partially tested the JJ-Synapse within our cryocooler system, enabling high-speed inference implementation for SNNs.

A wireless controlled robotic insect with ultrafast untethered running speeds

Running speed degradation of insect-scale (less than 5 cm) legged microrobots after carrying payloads has become a bottleneck for microrobots to achieve high untethered locomotion performance. In this work, we present a 2-cm legged microrobot (BHMbot, BeiHang Microrobot) with ultrafast untethered running speeds, which is facilitated by the complementary combination of bouncing length and bouncing frequency in the microrobot’s running gait. The untethered BHMbot (2-cm-long, 1760 mg) can achieve a running speed of 17.5 BL s−1 and a turning centripetal acceleration of 65.4 BL s−2 at a Cost of Transport of 303.7 and a power consumption of 1.77 W. By controlling its two front legs independently, the BHMbot demonstrates various locomotion trajectories including circles, rectangles, letters and irregular paths across obstacles through a wireless control module. Such advancements enable the BHMbot to carry out application attempts including sound signal detection, locomotion inside a turbofan engine and transportation via a quadrotor.

Ultra Fast Deep Lane Detection With Hybrid Anchor Driven Ordinal Classification.

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problems of efficiency and challenging scenarios like severe occlusions and extreme lighting conditions. Inspired by human perception, the recognition of lanes under severe occlusions and extreme lighting conditions is mainly based on contextual and global information. Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at ultra fast speed and the problem of challenging scenarios. Specifically, we treat the process of lane detection as an anchor-driven ordinal classification problem using global features. First, we represent lanes with sparse coordinates on a series of hybrid (row and column) anchors. With the help of the anchor-driven representation, we then reformulate the lane detection task as an ordinal classification problem to get the coordinates of lanes. Our method could significantly reduce the computational cost with the anchor-driven representation. Using the large receptive field property of the ordinal classification formulation, we could also handle challenging scenarios. Extensive experiments on four lane detection datasets show that our method could achieve state-of-the-art performance in terms of both speed and accuracy. A lightweight version could even achieve 300+ frames per second(FPS). Our code is at https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2.

Metasurface‐Empowered Quantum Photonics

In recent decades, quantum information technologies have attracted significant attention and experienced rapid development due to their ultrafast processing speeds and high level of information security. A significant trend in this field of study involves the ongoing pursuit of integrating and miniaturizing quantum devices. Metasurfaces, which are artificially designed planar nanostructure arrays with versatile wavefront shaping capabilities, present a promising platform for the development of integrated photonic quantum devices by effectively controlling quantum light in multiple degrees of freedom. In this review, recent advances in the field of quantum photonics facilitated by metasurfaces, encompassing quantum light sources, manipulation and measurement of quantum states, and the intriguing functionalities of quantum metasurfaces, are summarized. Additionally, potential future directions for the advancement of quantum metasurfaces are discussed.

WO3 Nanoplates Decorated with Au and SnO2 Nanoparticles for Real-Time Detection of Foodborne Pathogens.

Nowadays, metal oxide semiconductor gas sensors have diverse applications ranging from human health to smart agriculture with the development of Internet of Things (IoT) technologies. However, high operating temperatures and an unsatisfactory detection capability (high sensitivity, fast response/recovery speed, etc.) hinder their integration into the IoT. Herein, a ternary heterostructure was prepared by decorating WO3 nanoplates with Au and SnO2 nanoparticles through a facial photochemical deposition method. This was employed as a sensing material for 3-hydroxy-2-butanone (3H-2B), a biomarker of Listeria monocytogenes. These Au/SnO2-WO3 nanoplate-based sensors exhibited an excellent response (Ra/Rg = 662) to 25 ppm 3H-2B, which was 24 times higher than that of pure WO3 nanoplates at 140 °C. Moreover, the 3H-2B sensor showed an ultrafast response and recovery speed to 25 ppm 3H-2B as well as high selectivity. These excellent sensing performances could be attributed to the rich Au/SnO2-WO3 active interfaces and the excellent transport of carriers in nanoplates. Furthermore, a wireless portable gas sensor equipped with the Au/SnO2-WO3 nanoplates was assembled, which was tested using 3H-2B with known concentrations to study the possibilities of real-time gas monitoring in food quality and safety.

Engineering topological interface states in metal-wire waveguides for broadband terahertz signal processing.

Innovative terahertz waveguides are in high demand to serve as a versatile platform for transporting and manipulating terahertz signals for the full deployment of future six-generation (6G) communication systems. Metal-wire waveguides have emerged as promising candidates, offering the crucial advantage of sustaining low-loss and low-dispersion propagation of broadband terahertz pulses. Recent advances have opened up new avenues for implementing signal-processing functionalities within metal-wire waveguides by directly engraving grooves along the wire surfaces. However, the challenge remains to design novel groove structures to unlock unprecedented signal-processing functionalities. In this study, we report a plasmonic signal processor by engineering topological interface states within a terahertz two-wire waveguide. We construct the interface by connecting two multiscale groove structures with distinct topological invariants, i.e., featuring a π-shift difference in the Zak phases. The existence of this topological interface within the waveguide is experimentally validated by investigating the transmission spectrum, revealing a prominent transmission peak in the center of the topological bandgap. Remarkably, we show that this resonance is highly robust against structural disorders, and its quality factor can be flexibly controlled. This unique feature not only facilitates essential functions such as band filtering and isolating but also promises to serve as a linear differential equation solver. Our approach paves the way for the development of new-generation all-optical analog signal processors tailored for future terahertz networks, featuring remarkable structural simplicity, ultrafast processing speeds, as well as highly reliable performance.

Synergistic effect of charge transfer and interlayer swelling in V2CTx/SnS2 driving ultrafast and highly sensitive NO2 detection at room temperature

Transition metal carbides/nitrides (MXenes) have shown great potential in room temperature (RT) gas sensors, but the self-stacking of MXene sheets limits their sensitivity and response/recovery speed. In addition, the poor selectivity of MXenes sensors caused by the conventional single sensing mechanism remains a challenge. Herein, a synergistic effect of charge transfer and interlayer swelling is proposed to comprehensively improve the gas sensing performance of the MXenes toward oxidizing NO2. To this end, a resistive gas sensor is prepared by combining the accordion-like V2CTx MXene and n-type SnS2 nanoplates. The results reveal that the NO2 sensitivity of the V2CTx/SnS2 sensor is enhanced by 581.6 times compared to the pristine V2CTx sensor, while achieving ultrafast response and recovery speeds (4.8/4.7 s) at 25 °C under 51.9 % relative humidity. Furthermore, the V2CTx/SnS2 sensor exhibits excellent selectivity (response ratio > 5) to NO2. The outstanding gas sensing performances can be attributed to the synergistic sensing of charge transfer and interlayer swelling, unique structure and more active sites. This work expands the gas sensing application of the V2CTx MXene and promotes the in-depth combination of multiple sensing mechanisms, opening an effective avenue for developing high-performance RT NO2 sensors.

PanDepth, an ultrafast and efficient genomic tool for coverage calculation.

Coverage quantification is required in many sequencing datasets within the field of genomics research. However, most existing tools fail to provide comprehensive statistical results and exhibit limited performance gains from multithreading. Here, we present PanDepth, an ultra-fast and efficient tool for calculating coverage and depth from sequencing alignments. PanDepth outperforms other tools in computation time and memory efficiency for both BAM and CRAM-format alignment files from sequencing data, regardless of read length. It employs chromosome parallel computation and optimized data structures, resulting in ultrafast computation speeds and memory efficiency. It accepts sorted or unsorted BAM and CRAM-format alignment files as well as GTF, GFF and BED-formatted interval files or a specific window size. When provided with a reference genome sequence and the option to enable GC content calculation, PanDepth includes GC content statistics, enhancing the accuracy and reliability of copy number variation analysis. Overall, PanDepth is a powerful tool that accelerates scientific discovery in genomics research.

Third-order nonlinear optics and optical limiting of phenanthrenone derivatives

Organic nonlinear optical materials have attracted much attention due to their large nonlinear optical coefficients and ultrafast response speeds, as well as their potential applications in optical limiting (OL). Among them, nonlinear optical materials with phenanthrenone as the backbone are consistently understudied. We have successfully realized the transformation of organic small molecules in the nonlinear optical direction by replacing the carbonyl group on the structure of phenanthrenone with a malononitrile group. The nonlinear optical properties of CN-2 were investigated under different excitation conditions at 532 nm and the kinetic process of nonlinear optics was analyzed by transient absorption spectroscopy. In addition, we also investigated the OL properties of CN-2, and the OL experiments showed that CN-2 with a lower OL threshold has superior OL properties than C60.

High energy storage performance of (1-x)Ba0.5Sr0.5TiO3-xK0.5Na0.5NbO3 ceramics via a combined strategy of fine grains and multiphase polar nanoregions

Electrostatic dielectric capacitors have received great attention due to their ultrafast charging-discharging speed and ultrahigh power density. However, the low energy storage density and efficiency are the main obstacles to their practical applications. In this work, solid solution relaxor ceramics with different proportions of lead-free ferroelectrics Ba0.5Sr0.5TiO3 (BST) and K0.5Na0.5NbO3 (KNN) were prepared. Superior energy storage performance was achieved in the 0.7BST-0.3KNN ceramics with a breakdown strength (Eb) of 510 kV/cm, a recoverable energy storage density (Wrec) of 4.10 J/cm3, and an energy storage efficiency (η) of 80 %, which was fairly stable over the temperature range of 30–100 °C. Since multiple cations with different valence and radius coexist on the equivalent sites (A or B sites) of the solid solutions, the chemical disorder and the corresponding lattice distortion induced the coexistence of cubic, tetragonal, and orthorhombic phases, as well as the appearance of the multiphase polar nanoregions (PNRs), making the ferroelectric hysteresis loops linear. At the same time, ultrafine grains in the ceramics greatly improved the Eb. For achieving excellent energy storage performance, this work demonstrates a potential candidate of (1-x)BST-xKNN lead-free solid solution ceramics and a combined strategy of fine grains and multiphase PNRs.

BSAlign: A Library for Nucleotide Sequence Alignment.

Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic programming (DP) algorithms (e.g., Smith-Waterman and Needleman-Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment. Many optimization efforts that aim to accelerate the alignment process generally come from three perspectives: redesigning data structures [e.g., diagonal or striped Single Instruction Multiple Data (SIMD) implementations], increasing the number of parallelisms in SIMD operations (e.g., difference recurrence relation), or reducing search space (e.g., banded DP). However, no methods combine all these three aspects to build an ultra-fast algorithm. In this study, we developed a Banded Striped Aligner (BSAlign) library that delivers accurate alignment results at an ultra-fast speed by knitting a series of novel methods together to take advantage of all of the aforementioned three perspectives with highlights such as active F-loop in striped vectorization and striped move in banded DP. We applied our new acceleration design on both regular and edit distance pairwise alignment. BSAlign achieved 2-fold speed-up than other SIMD-based implementations for regular pairwise alignment, and 1.5-fold to 4-fold speed-up in edit distance-based implementations for long reads. BSAlign is implemented in C programing language and is available at https://github.com/ruanjue/bsalign.

A Strain‐Temperature Integrated Polymer‐Derived SiCN Ceramic High Temperature Sensor with Wide‐Range and Ultra‐Short Response Time

AbstractFabricating strain sensors with high response in a wide temperature and strain range is still a challenge, especially in high temperature harsh environments. Herein, a dual‐functional sensor with cantilever beam structure is fabricated using polymer‐derived SiCN ceramics, which can be applied for temperature and stain monitor. The performance of the integrated sensor is evaluated from room temperature to 1000 °C and the related sensing mechanism is analyzed. The prepared strain sensor has ultrafast response of 60 ms in the strain range of 0–3500 µε and the maximum strain rate can reach 46 200 µε s−1. Moreover, the sensor has excellent stability and ultra‐high sensitivity under high temperature environments. Additionally, the temperature can be detected separately up to 1000 °C without any disturbance. This attractive integration sensor with competitive sensitivity, excellent stability, ultrafast response speed, and broad range, has great potential to detect temperature and strain signals for practical application in high temperature extreme environments.

Reverse engineering and analysis of microstructure polymer fiber via artificial neural networks: simplifying the design approach

Abstract Microstructure polymer fibers have been extensively researched for their applications in various fields. The design and simulation of these fibers have utilized time-consuming techniques like the finite-difference time-domain and finite element method to facilitate the process. This study proposes an optimal artificial neural network (ANN) for predicting the structural design required to achieve desired optical properties. The ANN model takes various optical properties, including confinement loss, effective index, effective mode area, and wavelengths, as inputs to predict fiber design parameters such as diameter and pitch. To address the challenge of skewed distributions, a data set with a Gaussian-like distribution for confinement loss was generated using a logarithmic transformation method, enabling effective training of machine learning models. Furthermore, the ANN model demonstrates its capability to rapidly predict unknown geometric parameters using only the core mode properties of a polymer fiber, achieving results in a significantly shorter time (3 ms) compared to the trial-and-error approach of finite element method simulation (15 s). The reverse engineering model achieves a mean square error of 3.4877 × 10−06 with five hidden layers. The ANN model not only offers ultrafast calculation speed but also delivers high prediction accuracy, thereby accelerating the design process of optical devices. The differentiation among the prediction result, target, and calculation result provides compelling evidence that the proposed approach is an effective methodology for designing microstructure polymer fibers.

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawing inspiration from the transport nature of Na+ and K+ in synapses, here, a Li-based memristor (LixAlOy) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and K. The Li-based memristor exhibits ∼8 ns ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation, and reproducible switching behavior, enabled by lithium vacancies forming a conductive filament mechanism. Moreover, these memristors are capable of simulating fundamental behaviors of a biological synapse, including long-term potentiation and long-term depression behaviors. Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF) neuron is built using LixAlOy memristors, successfully integrating synaptic signals from both temporal and spatial levels and achieving an optimal threshold of SNNs. A computationally efficient TT-LIF-based SNN algorithm is also implemented for image recognition schemes, featuring a high recognition rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times lower than those of other memristor-based SNNs. Our studies reveal the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid switching and the construction of SNNs.

Loop Conductance Adapting Methodology to Suppress Switching Oscillations in High-Frequency GaN-Based Circuits

Gallium nitride (GaN) devices have enormous potentials in electric vehicle (EV) applications. The trend of high frequency and high power density help increase EV mileage level greatly. However, due to the ultra-fast switching speed of the GaN devices and low damping level in the compact circuit, switching oscillations are prone to happen and sometimes even sustain, which affects the circuit stability heavily. In this research, loop conductance adapting methodology is proposed to solve this problem, which is inserting a loop conductance adapter (LCA) into the GaN-based circuit. The LCA can be in manifold forms and inserted into different positions. A modified negative conductance model is established, which is universal to all the forms and inserting positions. Based on the established model and a derived stability criterion, the optimized design method is proposed, with which the most appropriate LCA parameters could be calculated and the corresponding suppression effects could be predicted. The details of the design method are introduced with three typical LCA examples. All the theoretical achievements are verified with LTspice simulations and experiments. In addition, the three examples are compared with each other according to suppression effects and power losses. Suggestions about LCA inserting position selection are also proposed.

VCSEL-based photonic spiking neural networks for ultrafast detection and tracking

Inspired by efficient biological spike-based neural networks, we demonstrate for the first time the detection and tracking of target patterns in image and video inputs at high-speed rates with networks of multiple artificial spiking optical neurons. Using photonic systems of in-parallel spiking vertical cavity surface emitting lasers (VCSELs), we demonstrate the implementation of multiple convolutional kernel operators which, in combination with optical spike signalling, enable the detection and tracking of target features in images/video feeds at an ultrafast photonic operation speed of 1 ns per pixel. Alongside a single layer optical spiking neural network (SNN) demonstration, a multi-layer network of photonic (GHz-rate) spike-firing neurons is reported where the photonic system successfully tracks a large complex feature (Handwritten Digit 3). The consecutive photonic layers perform spike-enabled image reduction and convolution operations, and interact with a software-implemented SNN, that learns the feature patterns that best identify the target to provide a high detection efficiency even in the presence of a distractor feature. This work therefore highlights the effectiveness of combining neuromorphic photonic hardware and software SNNs, for efficient learning and ultrafast operation, thanks to the use of spiking light signals, towards tackling complex AI and computer vision problems.

Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach.

A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension - frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile.

Integrating interface engineering and plasmonic effect to boost photoresponse performance of ZnO:Ga/GaN heterojunction self-powered UV photodetectors

The ability to function without an external power supply makes photodetectors highly promising for use in a new generation of wirelessly and independently operated ultraviolet (UV) photodetection and imaging systems. However, self-powered photodetectors' high dark current, inadequate light harvesting, and poor carrier separation prevent them from attaining high-efficiency UV photodetection and imaging. Herein, a high-performance heterojunction UV photodetector consisting of a single Ga-doped ZnO microwire enshrouded by Ag nanowires (AgNWs@ZnO:Ga MW), a MgO electron blocking layer, and a p-type GaN wafer was proposed. Taking full advantage of the interface engineering and plasmonic effects, the AgNWs@ZnO:Ga MW/MgO/GaN photodetector achieves high photodetection performance with an excellent responsivity of 262 mA/W, a remarkable external quantum efficiency of 92.37%, and a high detectivity of 1.966 × 1012 Jones under 370 nm illumination with power density of 0.22 mW/cm2. Furthermore, it also demonstrates ultrafast pulsed photoresponse speed with a rise/fall time of 3.64 μs/252 μs. An UV imaging system is designed based on the AgNWs@ZnO:Ga MW/MgO/GaN self-powered photodetector, allowing for high-quality, accurate, and distinct object imaging. This work combines interface engineering and plasmonic effects for achieving high-performance UV photodetection, which provides a strategic approach to the development of highly efficient optoelectronic devices.

Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing.

In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.