High-speed Processing Research Articles

Dynamic visualization study of in situ cleaning of polystyrene microparticles off silicon wafer surface

With the improvement of chip performance, the requirements for cleaning the surface of silicon wafers are becoming higher. However, due to equipment and technology, it is difficult to observe the complex motion processes of particles at the microscopic scale. In this paper, an in situ dynamic visualization experiment on the cleaning of Polystyrene Latex (PSL) on the surface of silicon wafers is carried out by using a high-speed camera and image processing software. The mechanical behavior of PSL particles in fluid was investigated on a microscopic scale, and the trajectory and force of the polystyrene particles on the surface of the wafers were visualized, which provided a new perspective for understanding the complex cleaning process. Theoretical models were developed to explain the motion characteristics of the particles by calculating parameters such as van der Waals force, surface tension, and trailing force, and these models provide a theoretical basis for optimizing the cleaning process. There are four particle motion modes in the fluid: (1) interface capture, where the particles on the surface of silicon wafer are trapped by gas–liquid interface under surface tension; (2) particle collision, where the particles captured by the water film collide with the particles on the wafer surface to make the latter leave the silicon wafer; (3) jump attachment, where the particles jump and attach to the surface of the particle group under the action of lift; and (4) wall surface movement, where the particles start up under the action of water flow and then leave the silicon wafer quickly.

Dynamic deformation and fracture of brass: Experiments and dislocation-based model

In this work, we perform a comprehensive study of the dynamic deformation and fracture of brass, including Taylor tests with classical and profiled cylinders and ball throwing experiments reaching the strain rates of about (0.1−1)/μs, as well as atomistic and continuum-level numerical modeling. Molecular dynamics (MD) simulations are used to construct the equation of state (EOS) of brass and to study its fracture characteristics at shear deformation under negative pressure. An original model of fracture under combined tensile-shear loading is formulated, which takes into account both the accumulation of empty volume in the process of lattice loosening due to the lattice defect production in the course of plastic deformation and further mechanical growth of voids controlled by the dislocation plasticity. This atomic-scale model is transmitted to the macroscopic experiment-scale level and embedded into 3D dislocation plasticity model to describe the dynamic deformation and fracture of brass using the numerical scheme of smoothed particle hydrodynamics (SPH). A part of experimental data is used to find the optimal parameters of the dislocation plasticity model by means of the Bayesian global optimization method accelerated with the help of artificial-neural-network (ANN)-based emulator of the 3D model. Another part of experimental data is used to fit the fracture model parameter. The remaining experimental data, which are not used in the parameterization, are applied to verify the parameterized model. The developed physical-based model provides correct and meaningful description of the dynamic deformation and fracture of brass, while the developed formalized approach to its parameterization opens a way to wider use of this type of models in the engineering applications, including studies on dynamic performance and high-speed processing technologies.

A Field-Programmable Gate Array-Based Quasi-Cyclic Low-Density Parity-Check Decoder with High Throughput and Excellent Decoding Performance for 5G New-Radio Standards

This work presents a novel fully parallel decoder architecture designed for high-throughput decoding of Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes within the context of 5G New-Radio (NR) communication. The design uses the layered Min-Sum (MS) algorithm and focuses on increasing throughput to meet the strict needs of enhanced Mobile BroadBand (eMBB) applications. We incorporated a Sub-Optimal Low-Latency (SOLL) technique to enhance the critical check node processing stage inherent to the MS algorithm. This technique efficiently computes the two minimum values, rendering the architecture well-suited for specific Ultra-Reliable Low-Latency Communication (URLLC) scenarios. We design the decoder to be reconfigurable, enabling efficient operation across all expansion factors. We rigorously validate the decoder’s effectiveness through meticulous bit-error-rate (BER) performance evaluations using Hardware Description Language (HDL) co-simulation. This co-simulation utilizes a well-established suite of tools encompassing MATLAB/Simulink for system modeling and Vivado, a prominent FPGA design suite, for hardware representation. With 380,737 Look-Up Tables (LUTs) and 32,898 registers, the decoder’s implementation on a Virtex-7 XC7VX980T FPGA platform by AMD/Xilinx shows good hardware utilization. The architecture attains a robust operating frequency of 304.5 MHz and a normalized throughput of 49.5 Gbps, marking a 36% enhancement compared to the state-of-the-art. This advancement propels decoding capabilities to meet the demands of high-speed data processing.

Leveraging multiplexed metasurfaces for multi-task learning with all-optical diffractive processors

Abstract Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80 % for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems.

Scalable Synaptic Transistor Memory from Solution-Processed Carbon Nanotubes for High-Speed Neuromorphic Data Processing.

Neural networks as a core information processing technology in machine learning and artificial intelligence demand substantial computational resources to deal with the extensive multiply-accumulate operations. Neuromorphic computing is an emergent solution to address this problem, allowing the computation performed in memory arrays in parallel with high efficiencies conforming to the neural networks. Here, scalable synaptic transistor memories are developed from solution-sorted carbon nanotubes. The transistors exhibit a large switching ratio of over 105, a significant memory window of ≈12V arising from charge trapping, and low response delays down to tens of nanoseconds. These device characteristics endow highly stabilized reconfigurable conductance states, successful emulation of synaptic functions, and a high data processing speed. Importantly, the devices exhibit uniform characteristic metrics, e.g., with a 1.8% variation in the memory window, suggesting an industrial-scale manufacturing capability of the fabrication. Using the memories, a hardware convolution kernel is designed and parallel image processing is demonstrated at a speed of 1M bit per second per input channel. Given the efficacy of the convolution kernel, a promising prospect of the memories in implementing neuromorphic computing is envisaged. To explore the potential, large-scale convolution kernels are simulated and high-speed video processing is realized for autonomous driving.

Optoelectronic Reconfigurable Logic Gates Based on Two-Dimensional Vertical Field-Effect Transistors.

Optoelectronic reconfigurable logic gates are promising candidates to meet the multifunctional and energy-efficient requirements of integrated circuits. However, complex device architectures need more power and hinder multifunctional device applications. Here, we design vertical field-effect transistors (VFET) based on the two-dimensional (2D) graphene/MoS2/WSe2/graphene van der Waals heterojunction forming ohmic and Schottky contact. The modulation of the Schottky barrier via gate bias enables the device to switch between positive and negative photocurrents, which can effectively achieve optoelectronic reconfigurable logic gates (XNOR, NOR, NAND, AND, OR, and Inhibit) in a single device. Particularly, the transistor number is reduced by 75% for ("XNOR", "NOR", "NAND") and 83% for ("AND", "OR") gates compared to traditional logic circuits. This work provides a promising route for using a single device to realize optoelectronic reconfigurable logic gates, which can advance the development of high-speed, high-throughput, and intricate data processing in future optical computing applications.

Growing Semiconductor Industry in India

This paper delves into the growing semiconductor industry in India, and its importance in every aspect of present and future high data processing speed. Semiconductors are crucial electronic components for quantum computing, artificial intelligence (AI), medical devices, aerospace and defence systems, etc. Thus, the use of semiconductors for these most important and future ease concepts, this paper is an understanding approach to the growing importance of semiconductors in India

Automated Surface Crack Identification of Reinforced Concrete Members Using an Improved YOLOv4-Tiny-Based Crack Detection Model

In recent times, the deployment of advanced structural health monitoring techniques has increased due to the aging infrastructural elements. This paper employed an enhanced You Only Look Once (YOLO) v4-tiny algorithm, based on the Crack Detection Model (CDM), to accurately identify and classify crack types in reinforced concrete (RC) members. YOLOv4-tiny is faster and more efficient than its predecessors, offering real-time detection with reduced computational complexity. Despite its smaller size, it maintains competitive accuracy, making it ideal for applications requiring high-speed processing on resource-limited devices. First, an extensive experimental program was conducted by testing full-scale RC members under different shear span (a) to depth ratios to achieve flexural and shear dominant failure modes. The digital images captured from the failure of RC beams were analyzed using the CDM of the YOLOv4-tiny algorithm. Results reveal the accurate identification of cracks formed along the depth of the beam at different stages of loading. Moreover, the confidence score attained for all the test samples was more than 95%, which indicates the accuracy of the developed model in capturing the types of cracks in the RC beam. The outcomes of the proposed work encourage the use of a developed CDM algorithm in real-time crack detection analysis of critical infrastructural elements.

Selection of parameters of high-speed thermodeformation of Cr–Ni–Mo steel processing on the basis of imitation modeling

The temperature of the end of finishing stage, cooling rate and coil winding temperature are structure-sensitive parameters for bainite-grade steels in the production of plate less than 10 mm thick. Simulation modeling of high-speed thermo-deformation processing of Cr–Ni–Mo bainitic steel samples has been carried out in order to select the most rational technological modes in continuous hot rolling mills. The influence of strain rate and temperature as well as niobium and vanadium microalloying additives on phase transformations has been investigated.

Study on the Electronic Design Automation of Taximeter

The integration of Field Programmable Gate Arrays (FPGAs) into the design of digital taximeters signifies a transformative step forward in the field of Electronic Design Automation (EDA), catering to the evolving needs of urban transportation systems. This paper details the development and implementation of an FPGA-based digital taximeter that aims to enhance fare calculation accuracy, operational reliability, and adaptability to regulatory changes through the use of sophisticated technology. FPGAs are chosen for their ability to perform high-speed processing and to be reprogrammed in the field, which allows the taximeter system to adapt quickly to new fare structures and transportation regulations.

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.

Structural dynamics of individual plant fibres: a contribution to the understanding of damping properties of bio-based composites

The field of bio-based composites faces challenges in understanding the physical mechanisms providing excellent compromise between stiffness and damping. Potential sources of damping, including the matrix, the fibre, and the interfaces (fibres/fibres and fibres/matrix) are identified in the literature. To better understand damping at the composite scale, it is necessary to understand the dissipation causes at each scale of the material. However, the mechanical characteristics of individual plant fibres, such as modulus and damping, can be uncertain due to the complexity of measuring their properties at such small scale (10 µm diameter, a few mm long). To address this knowledge gap, this work proposes a method based on dynamic response of individual plant fibres. By exciting the fibre with a piezoelectric actuator and using high-speed camera image processing to evaluate displacements along the fibre, it becomes possible to determine intrinsic dynamic properties such as the loss factor and storage modulus. Controlled environment tests are conducted to investigate the influence of external parameters such as temperature and pressure on the measurement results. By utilizing this method, the aim is to obtain a better understanding of the damping properties of plant fibres and ultimately improve design choices for bio-based composite materials.

Система ввода цифровых предыскажений на основе обобщенной полиномиальной модели с памятью для GaN усилителей мощности

Modern power amplifiers based on GaN transistors face a number of problems related to their physical properties and operating conditions. The high output power of such power amplifiers is accompanied by significant nonlinear distortions, especially when operating in saturation mode. Amplifiers based on GaN transistors have pronounced «short» memory effects, which leads to the dependence of current distortions on previous signal states. At the same time, support for broadband signals requires high processing speeds and large computing resources. Classical digital pre-distortion methods do not always cope with the task of effectively compensating for real-time distortion for power amplifiers based on GaN transistors. These problems complicate the process of linearization of power amplifiers based on GaN transistors and require the development of new methods that can effectively compensate for non-linearities and memory effects without significantly increasing the cost and complexity of the system. The purpose of the work is to create and analyze the effectiveness of a digital pre-imaging system based on a generalized polynomial model with memory to improve the linearity and efficiency of power amplifiers based on GaN transistors in wireless communication systems. The paper presents the architecture of the digital preimage input system based on a generalized polynomial model with memory. The use of this model makes it possible to improve the efficiency of suppressing nonlinear distortions at the output of a power amplifier based on GaN transistors. Practical significance – the proposed architecture can be implemented on FPGAs, VLSI or SOC with a minimum amount of computing resources.

Proposal and performance evaluation of a new parallel plate continuous cell separation device using dielectrophoresis.

Along with the rapid development of cellular biological research in recent years, there has been an urgent need for a high-speed, high-precision method of separating target cells from a highly heterogeneous cell population. Among the various cell separation technologies proposed so far, dielectrophoresis (DEP)-based approaches have shown particular promise because they are noninvasive to cells. We have developed a new DEP-based device to separate large numbers of live and dead cells of the human mammary cell line MCF10A. In this study, we validated the separation performance of this device. The results showed the successful separation of a higher percentage of cells than in previous studies, with a separation efficiency higher than 90%. In the past, there have been no confirmed cases in which a separation rate of over 90% and high-speed processing of a large number of cells were simultaneously achieved. It was shown that the proposed device can process large numbers of cells at high speed and with high accuracy.

Augmented Reality and Virtual Reality: A New Way of Seeing the World

Technologies such as (AR) Augmented Reality &(VR) Virtual Reality, which provide immersive digital experiences, interactive environments, simulation, and engagement, have completely changed how we approach learning. However, in order to meet the huge demand in education, these technologies which are still in the emerging stage need to be heavily customized and heavily invested in. This thorough analysis seeks to contextualize the last few years of development of Virtual and Augmented Reality in the Education. For additional study, a total one thousand five hundred and thirty-six articles are chosen using text mining and theme analysis techniques. Based on earlier research on AR and VR in education, hypotheses were developed and are currently being processed and analysed to reveal the current development in literature via Augmented Reality and Virtual Reality, applications, benefits, and future directions. The findings show that wearable technology has contributed significantly to the exponential expansion in the use of Virtual and Augmented Reality in education in recent years. Results also highlight the need for faster adoption and customization of these technologies in educational institutions, based on secondary data. An increasing number of educational applications for the learning process are emerging as Virtual and Augmented Realityexp and quickly. It is advised that researchers stay up to date on the gaps in AR and VR’s Changeover to education &develop practical adaptation strategies to maximize the benefits of these technological development with significant developments in high-speed transmission and processing, AR &VR are developing as Display Platforms for Next Generation for more intimate human-digital interactions. Nonetheless, matching the extraordinary Human Vision Performance while keeping Near the Eye Display module tiny and lightweight presents unprecedented hurdles for optical engineering. Fortunately, new advances in Holographic Optical Elements HOEs and Lithography Enable Devices present novel approaches to overcoming these challenges in Virtual and Augmented Reality which would otherwise be problematic with traditional optics

Comparison of YOLOv8 Models for Aircraft Detection in Airport Apron Using Digital Image Processing

Airport safety can be improved by efficient air traffic management, which monitors aircraft parking spots and controls their movement into them more efficiently. In addition to emergency situations, it can assist in accurately inspecting airport areas. Management costs can be reduced by reducing the workload of personnel in controlling and helping manage air and ground traffic. This research focuses on comparing the performance of various YOLOv8 models (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8nl, and YOLOv8nx) in an automated aircraft detection system using digital image processing techniques. The methodology involves collecting a dataset of 1,000 airport apron images with parked aircraft, dividing them into 900 training images and 100 testing images. The YOLOv8 models are trained on the training dataset, and their performance is evaluated on the testing dataset using confusion matrices. Experimental results reveal that YOLOv8nx achieves the highest average aircraft detection performance, with a precision of 0.94, recall of 0.74, and f1-score of 0.83. Additionally, YOLOv8n demonstrates the highest processing speed at 0.95 milliseconds. Consequently, YOLOv8n is suitable for applications requiring high-speed processing, while YOLOv8nx is ideal for tasks demanding the utmost performance efficiency.

Influencing the powder particle transport in high-speed laser melt injection

Using high-speed laser melt injection (HSLMI), it is possible to generate wear-resistant metal matrix composite (MMC) surfaces on tools with great productivity. Since high laser intensities are required for reaching high process speeds, strong interactions can occur between powder particles and the laser beam. In order to reduce the interaction time and gain a better understanding of the role of particle transport in the HSLMI process, trajectories of spherical fused tungsten carbide (SFTC) particles were analyzed using high-speed imaging. The trajectories were divided into a path outside the laser beam and a path inside the laser beam. The identified interaction mechanisms were particle deformations, the formation of agglomerates, and particle disintegration. The volume flow rate of the feeding gas was found to have a decisive influence on the travel time of the particles, whereas the powder feed rate and the working distance of the powder nozzle only had a minor influence. Consequently, an increased volume flow rate led to a significant reduction of interactions between SFTC particles and the laser beam.

Wafer-scale fabrication of InGaP-on-insulator for nonlinear and quantum photonic applications

The development of manufacturable and scalable integrated nonlinear photonic materials is driving key technologies in diverse areas, such as high-speed communications, signal processing, sensing, and quantum information. Here, we demonstrate a nonlinear platform—InGaP-on-insulator—optimized for visible-to-telecommunication wavelength χ(2) nonlinear optical processes. In this work, we detail our 100 mm wafer-scale InGaP-on-insulator fabrication process realized via wafer bonding, optical lithography, and dry-etching techniques. The resulting wafers yield 1000 s of components in each fabrication cycle, with initial designs that include chip-to-fiber couplers, 12.5-cm-long nested spiral waveguides, and arrays of microring resonators with free-spectral ranges spanning 400–900 GHz. We demonstrate intrinsic resonator quality factors as high as 324 000 (440 000) for single-resonance (split-resonance) modes near 1550 nm corresponding to 1.56 dB/cm (1.22 dB/cm) propagation loss. We analyze the loss vs waveguide width and resonator radius to establish the operating regime for optimal 775–1550 nm phase matching. By combining the high χ(2) and χ(3) optical nonlinearity of InGaP with wafer-scale fabrication and low propagation loss, these results open promising possibilities for entangled-photon, multi-photon, and squeezed light generation.

Bionic firing activities in a dual mem-elements based CNN cell

Firing activities provide the potential possibility for achieving bio-brain functionality with high energy-efficient and high-speed information processing performance. This inspires the design of bionic circuits to generate firing activities and develop brain-like applications. In this paper, a dual mem-elements based cellular neural network (CNN) cell is constructed to produce bionic firing activities, in which a non-ideal memcapacitor and an N-type locally active memristor are employed to emulate the functions of the neuronal membrane. The proposed CNN cell has an excitation-dependent equilibrium trajectory and stability. Numerical analysis shows that the dual mem-elements based CNN cell has abundant dynamical behaviors of forward/reverse period-doubling bifurcation routes, chaos crisis, tangent bifurcation, and bubbles with the change of model parameters of the CNN cell, memcapacitor, and exciting source. As a result, the rich firing patterns’ transition can be observed from the two-dimensional dynamics evolution. The analog circuit of the proposed CNN cell is designed, and then a PCB-based hardware circuit is implemented. The experimental results certify the accuracy of the theoretical and numerical analysis.

A - 5 Longitudinal Analysis of Protective Factors on Cognition Following Total Knee Arthroplasty

Abstract Objective To examine cognitive and brain resilience factors on older adult cognition following total knee arthroplasty (TKA). Methods Participants were recruited through orthopedic clinics and screened for eligibility (i.e., age > 60 years, English as first language, diagnosis of knee osteoarthritis, intact activities of daily living). Data were collected before surgery, three weeks, three months, and one-year post-TKA (N = 67; mean(SD) age = 69.49(7.09) years; mean(SD) education = 15.62(2.83) years; 51% female; 90% non-Hispanic White (NHW), 10% non-Hispanic Black. Participants received baseline magnetic resonance imaging (3 T Siemens Verio). Magnetization-prepared rapid gradient-echo images were preprocessed. Machine learning predicted brain age (DeepBrainNet) was calculated for each participant. Linear mixed effects models examined longitudinal effects of word reading (Wide Range Achievement Test-III (WRAT-III)), brain age gap (predicted brain age minus chronological age), sex, ethnicity/race, pain interference (Brief Pain Inventory), and depression (Beck Depression Inventory-II) on cognitive composites (i.e., memory, working memory/inhibition, reasoning, processing speed, language). Results Memory, working memory/inhibition, reasoning, and processing speed had curvilinear trajectories (p’s < 0.05), declining post-TKA and subsequently returning to baseline. Word reading positively associated with memory (β = 0.065, SE = 0.016, p < 0.001), working memory/inhibition (β = 0.069, SE = 0.016, p < 0.001), reasoning (β = 0.099, SE = 0.021, p < 0.001), and processing speed (β = 0.050, SE = 0.015, p < 0.01). Language negatively associated with BAG (β = −0.041, SE = 0.017, p < 0.05). Women had higher processing speed (β = −0.285, SE = 0.135, p < 0.05), and NHW participants had higher memory (β = 0.479, SE = 0.224, p < 0.05). When pain interference was higher, processing speed (β = −0.003, SE = 0.002, p < 0.05) and language (β = −0.007, SE = 0.002, p < 0.01) were lower. Conclusions Cognitive and brain resilience measures may identify individuals protected against cognitive dysfunction post-surgically. Future research increasing representation in underrepresented groups is warranted.