Hybrid Integration Research Articles

Application of the hybrid integration displacement algorithm based on CEEMDAN and wavelet threshold denoising in vibration screening equipment

Abstract Vibration screening equipment has an extensive application profile in material screening, in which the displacement parameters can reveal the motion state of the material and affect the screening efficiency. These displacement parameters can be obtained by integrating the acceleration signal of the equipment. In this paper, to prevent the noise in the acceleration signal from further amplifying its negative effects on the subsequent integration, the acceleration signal is preprocessed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising. Besides, a hybrid integration displacement algorithm is utilized to mitigate the influence of integration errors. The consistent results between simulation and platform experiments demonstrate that CEEMDAN in combination with wavelet threshold denoising can effectively remove noise while retaining the main frequency signal. In addition, the displacement signal obtained by the hybrid integration algorithm proposed in this paper is closer to the original displacement signal. Compared with the 2nd time-domain integration, the 2nd frequency-domain integration, and the empirical mode decomposition (EMD) integration methods, the integration method proposed in this paper achieves a smaller peak error, mean absolute error (MAE), and root mean square error (RMSE). The experimental results corroborate the superiority of this method in the application of vibration screening equipment.

High-dimensional spin-orbital single-photon sources.

Hybrid integration of solid-state quantum emitters (QEs) into nanophotonic structures opens enticing perspectives for exploiting multiple degrees of freedom of single-photon sources for on-chip quantum photonic applications. However, the state-of-the-art single-photon sources are mostly limited to two-level states or scalar vortex beams. Direct generation of high-dimensional structured single photons remains challenging, being still in its infancy. Here, we propose a general strategy to design highly entangled high-dimensional spin-orbital single-photon sources by taking full advantage of the spatial freedom to design QE-coupled composite (i.e., Moiré/multipart) metasurfaces. We demonstrate the generation of arbitrary vectorial spin-orbital photon emission in high-dimensional Hilbert spaces, mapping the generated states on hybrid-order Bloch spheres. We further realize single-photon sources of high-dimensional spin-orbital quantum emission and experimentally verify the entanglement of high-dimensional superposition states with high fidelity. We believe that the results obtained facilitate further progress in integrated solutions for the deployment of next-generation high-capacity quantum information technologies.

Hybrid and Electric Vehicle (EV) integration in public transport: Challenges and opportunities

As cities worldwide grapple with the dual challenges of increasing urbanization and climate change, the integration of hybrid and electric vehicles (EVs) into public transport systems emerges as a critical solution. This article examines the multifaceted challenges and opportunities associated with adopting hybrid and EV technologies for buses and coaches. Key challenges include the high initial costs, the need for robust infrastructure, and concerns regarding vehicle reliability. Conversely, the transition offers significant benefits, such as substantial emissions reductions, improved operational efficiency, and enhanced public perception of sustainable practices. Through an evaluation of successful case studies from cities like London, Amsterdam, and Shenzhen, this article highlights effective strategies and outcomes achieved in the realm of public transport electrification. Ultimately, the findings underscore the necessity for collaborative efforts among stakeholders to drive the successful integration of hybrid and EV technologies, paving the way for a more sustainable urban mobility future.

An 8-channel narrow linewidth multi-wavelength laser hybrid integrated by photonic wire bonding structures for DWDM systems

An 8-channel monolithically integrated narrow linewidth multi-wavelength laser array (NL-MLA) combined with the commercial 8 × 1 planar lightwave circuit (PLC) coupler by the photonic wire bonding (PWB) technique is demonstrated in this paper. A distributed feedback (DFB) laser with the high-reflection and anti-reflection coatings (HR-AR) and the tapered asymmetric corrugation-pitch-modulated (TACPM) grating structure is proposed. The TACPM grating is achieved by the reconstruction equivalent chirp (REC) technique, which is low-cost and of high wavelength accuracy. According to the numerical simulation results, under the unavoidable impact of the facet phase of the HR end, the TACPM DFB laser suppresses the longitudinal spatial hole burning (LSHB) effect significantly, while having better single-mode performance and control of wavelength compared with the conventional HR-AR DFB laser. An 8-channel HR-AR TACPM DFB NL-MLA is fabricated with the 0.8 nm wavelength spacing design. The hybrid integration between the 8-channel NL-MLA and the PLC chip is achieved using the PWB technique. Both good single-mode performance and accurate wavelength interval of 0.8 nm can be obtained when all channels work simultaneously. The laser linewidth is below 273.8 kHz with good uniformity, and the relative intensity noise is under −159.6 dB/Hz for each channel.

Analytical solutions for the Noyes Field model of the time fractional Belousov Zhabotinsky reaction using a hybrid integral transform technique

In this work, we employed an attractive hybrid integral transform technique known as the natural transform decomposition method (NTDM) to investigate analytical solutions for the Noyes-Field (NF) model of the time-fractional Belousov–Zhabotinsky (TF-BZ) reaction system. The aforementioned time-fractional model is considered within the framework of the Caputo, Caputo–Fabrizio, and Atangana–Baleanu fractional derivatives. The NTDM couples the Adomian decomposition method and the natural transform method to generate rapidly convergent series-type solutions via an elegant iterative approach. The existence and uniqueness of solutions for the considered time-fractional model are first investigated via a fixed-point approach. The reliability and efficiency of the considered solution method is then demonstrated for two test cases of the TF-BZ reaction system. To demonstrate the validity and accuracy of the considered technique, numerical results with respect to each of the mentioned fractional derivatives are presented and compared with the exact solutions as well as with those from existing related literature. Graphical representations depicting the dynamic behaviors of the chemical wave profiles of the concentrations of the intermediates are presented with respect to varying fractional parameter values as well as temporal and spatial variables. The obtained results indicate that the execution of the method is straightforward and can be employed to explore nonlinear time-fractional systems modeling complex chemical reactions.

Polarization Independent Dynamic Beam Steering based on Liquid Crystal Integrated Metasurface

Digital Micromirror Devices, extensively employed in projection displays offer rapid, polarization-independent beam steering. However, they are constrained by microelectromechanical system limitations, resulting in reduced resolution, limited beam steering angle and poor stability, which hinder further performance optimization. Liquid Crystal on Silicon technology, employing liquid crystal (LC) and silicon chip technology, with properties of high resolution, high contrast and good stability. Nevertheless, its polarization-dependent issues lead to complex system and low efficiency in device applications. This paper introduces a hybrid integration of metallic metasurface with nematic LC, facilitating a polarization-independent beam steering device capable of large-angle deflections. Employing principles of geometrical phase and plasmonic resonances, the metallic metasurface, coupled with an electronically controlled LC, allows for dynamic adjustment, achieving a maximum deflection of ± 27.1°. Additionally, the integration of an LC-infused dielectric grating for dynamic phase modulation and the metasurface for polarization conversion ensures uniform modulation effects across all polarizations within the device. We verify the device’s large-angle beam deflection capability and polarization insensitivity effect in simulations and propose an optimization scheme to cope with the low efficiency of individual diffraction stages.

Food fraud prevention strategies: Building an effective verification ecosystem.

Food fraud is an ever-present threat that regulators, food business operators (FBOs), and consumers need to be aware of, prevent where possible, and address by developing mitigation strategies to detect and reduce its negative consequences. While extant literature focuses on food fraud detection, there is less attention given to prevention strategies, a knowledge gap this review seeks to address. The aim of this review was to consider food-related fraud prevention initiatives, understand what has worked well, and develop a series of recommendations on preventing food fraud, both policy related and for future research. Reactive (including intelligence based) food fraud detection dominates over prevention strategies, especially where financial, knowledge, and time resources are scarce. First-generation tools have been developed for food fraud vulnerability assessment, risk analysis, and development of food fraud prevention strategies. However, examples of integrated food control management systems at FBO, supply chain, and regulatory levels for prevention are limited. The lack of hybrid (public/private) integration of food fraud prevention strategies, as well as an effective verification ecosystem, weakens existing food fraud prevention plans. While there are several emergent practice models for food fraud prevention, they need to be strengthened to focus more specifically on capable guardians and target hardening. This work has implications for policymakers, Official Controls bodies, the food industry, and ultimately consumers who seek to consistently purchase food that is safe, legal, and authentic.

Ultrafast Silicon/Graphene Optical Nonlinear Activator for Neuromorphic Computing

AbstractOptical neural networks (ONNs) have shown great promise in overcoming the speed and efficiency bottlenecks of artificial neural networks. However, the absence of high‐speed, energy‐efficient nonlinear activators significantly impedes the advancement of ONNs and their extension to ultrafast application scenarios like real‐time intelligent signal processing. In this work, a novel silicon/graphene ultrafast all‐optical nonlinear activator, leveraging the hybrid integration of silicon slot waveguides, plasmonic slot waveguides, and monolayer graphene is demonstrated. Exploiting the exceptional picosecond‐scale photogenerated carrier relaxation time of graphene, the response time of the activator is markedly reduced to ≈93.6 ps, establishing all‐optical activator as the fastest known in silicon photonics to knowledge. Moreover, the all‐optical nonlinear activator holds a low threshold power of 5.49 mW and a corresponding power consumption per activation of 0.51 pJ. Its feasibility and capability for use in ONNs, manifesting performance comparable with commonly used activation functions are experimentally confirmed. This breakthrough in speed and energy efficiency of all‐optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs.

Evaluating landslide susceptibility: the impact of resolution and hybrid integration approaches

The present study investigates the effectiveness of various landslide susceptibility machine learning (ML) models at multiple spatial resolutions. Using various conditioning factors, including topography, hydrology, and human influences, the study analyzed the predictive power of single, integrated, and comparative ML models. Alternating Decision Tree (ADT), Forest by Penalizing Attributes (FPA), and Random Forest (RAF), and integrated approaches such as Rotation Forest (RF) and Random Subspace (RS) that were based on ADT and FPA were utilized for the study. All models were trained and validated using a ten-fold cross-validation technique and evaluated by various statistical metrics, across resolutions from 12.5 m to 200 m. Shenmu City in China was chosen as an ideal test site to evaluate the developed methodology. The study reveals that finer spatial resolutions significantly enhance the accuracy of landslide predictions and that integrated models have superior performance over single models. The Frequency Ratio method identified elevation, slope, and hydrological factors as the key predictors concerning landslide occurrences. According to the results the RS-ADT model at a 12.5 m resolution achieved the highest evaluation accuracy (ROC = 0.907). The research highlights the possibility of combining multiple modeling techniques and high-resolution data to improve the predictive accuracy of landslide susceptibility models.

Modified Hybrid Integration Algorithm for Moving Weak Target in Dual-Function Radar and Communication System

Modified Hybrid Integration Algorithm for Moving Weak Target in Dual-Function Radar and Communication System

Research on flexible and fast tunable laser in coherent optical communications

A flexible and fast tunable laser based on hybrid integration is proposed, which can realize single and multi-wavelength reconfigurable output by controlling the on or off of semiconductor optical amplifiers (SOAs) array in combination with a tunable arrayed waveguide grating (AWG). The wavelength switching time achieves <0.8 ns. The stable output power of the tunable laser is >16 dBm with >70 dB side mode suppression ratio (SMSR) and 6.4 nm tunability. The designed laser exhibits narrow linewidths down to 1 kHz and longitudinal mode spacing of 0.8 nm, which is suitable for ITU-T standards and the output power meets the requirements of coherent optical communications. A broadened spot-size converter (SSC) for bi-directional transmission is designed and implemented with a power coupling efficiency about 0.93 and an optical bandwidth of more than 200 nm. Demonstration of the 100 Gb/s quadrature phase shift keying (QPSK) self-homodyne coherent optical transmission based on designed laser is achieved.

Innovative Integration of Dual Quantum Cascade Lasers on Silicon Photonics Platform.

For the first time, we demonstrate the hybrid integration of dual distributed feedback (DFB) quantum cascade lasers (QCLs) on a silicon photonics platform using an innovative 3D self-aligned flip-chip assembly process. The QCL waveguide geometry was predesigned with alignment fiducials, enabling a sub-micron accuracy during assembly. Laser oscillation was observed at the designed wavelength of 7.2 μm, with a threshold current of 170 mA at room temperature under pulsed mode operation. The optical output power after an on-chip beam combiner reached sub-milliwatt levels under stable continuous wave operation at 15 °C. The specific packaging design miniaturized the entire light source by a factor of 100 compared with traditional free-space dual lasers module. Divergence values of 2.88 mrad along the horizontal axis and 1.84 mrad along the vertical axis were measured after packaging. Promisingly, adhering to i-line lithography and reducing the reliance on high-end flip-chip tools significantly lowers the cost per chip. This approach opens new avenues for QCL integration on silicon photonic chips, with significant implications for portable mid-infrared spectroscopy devices.

Enhancing corporate bankruptcy prediction via a hybrid genetic algorithm and domain adaptation learning architecture

In the contemporary business landscape, accurately evaluating a company’s financial health is essential for stakeholders to mitigate risks and avert bankruptcy. This study presents an innovative approach to improving business bankruptcy prediction through the hybrid integration of Domain Adaptation Learning (DAL) and Genetic Algorithm (GA) techniques. The hybrid model harnesses DAL to address distributional changes in real-world scenarios and utilises GA’s proficiency in feature selection. Six machine learning models are rigorously evaluated against the proposed hybrid model: Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boosting (GB), k-Nearest Neighbours (k-NN), and Stacking Ensemble (SE). Our hybrid model performs well on imbalanced target datasets using the Area Under the Precision–Recall Curve metric: 0.93 (RF), 0.93 (SVM), 0.89 (LR), 0.91 (GB), 0.88 (k-NN), and 0.92 (SE). These findings highlight the model’s ability to overcome the limitations of traditional approaches, offering a more reliable predictive framework for stakeholders to make informed decisions and proactively manage financial stability. Future research directions may explore the applicability of this hybrid model across different industries and the integration of additional techniques to further enhance its performance.

TRACE: a code for time-reversible astrophysical close encounters

Abstract We present TRACE, an almost time-reversible hybrid integrator for the planetary N-body problem. Like hybrid symplectic integrators, TRACE can resolve close encounters between particles while retaining many of the accuracy and speed advantages of a fixed timestep symplectic method such the Wisdom–Holman map. TRACE switches methods time-reversibly during close encounters following the prescription of Hernandez &amp; Dehnen. In this paper we describe the derivation and implementation of TRACE and study its performance for a variety of astrophysical systems. In all our test cases TRACE is at least as accurate and fast as the hybrid symplectic integrator MERCURIUS. In many cases TRACE’s performance is vastly superior to that of MERCURIUS. In test cases with planet-planet close encounters, TRACE is as accurate as MECURIUS with a 12x speedup. If close encounters with the central star are considered, TRACE achieves good error performance while MERCURIUS fails to give qualitatively correct results. In ensemble tests of violent scattering systems, TRACE matches the high-accuracy IAS15 while providing a 15x speed-up. In large N systems simulating lunar accretion, TRACE qualitatively gives the same results as IAS15 but at a 41x speedup. We also discuss some cases such as von Zeipel-Lidov-Kozai cycles where hybrid integrators perform poorly and provide some guidance on which integrator to use for which system. TRACE is freely available within the REBOUND package.

Silica-Polymer Heterogeneous Hybrid Integrated Mach-Zehnder Interferometer Optical Waveguide Temperature Sensor.

In this paper, a temperature sensor based on a polymer-silica heterogeneous integrated Mach-Zehnder interferometer (MZI) structure is proposed. The MZI structure consists of a polymer waveguide arm and a doped silica waveguide arm. Due to the opposite thermal optical coefficients of polymers and silica, the hybrid integrated MZI structure enhances the temperature sensing characteristics. The direct coupling method and side coupling method are introduced to reduce the coupling loss of the device. The simulation results show that the side coupling structure has lower coupling loss and greater manufacturing tolerance compared to the direct coupling structure. The side coupling loss for PMMA material-based devices, NOA material-based devices, and SU-8 material-based devices is 0.104 dB, 0.294 dB, and 0.618 dB, respectively. The sensitivity (S) values of the three hybrid devices are -6.85 nm/K, -6.48 nm/K, and -2.30 nm/K, which are an order of magnitude higher than those of an all-polymer waveguide temperature sensor. We calculated the temperature responsivity (RT) (FSR→∞) of the three devices as 13.16 × 10-5 K, 32.20 × 10-5 K, and 20.20 × 10-5 K, suggesting that high thermo-optic coefficient polymer materials and the hybrid integration method have a promising application in the field of on-chip temperature sensing.

An Integrated Photonic Biosensing Platform for Pathogen Detection in Aquaculture.

Aquaculture is expected to play a vital role in solving the challenge of sustainably providing the growing world population with healthy and nutritious food. Pathogen outbreaks are a major risk for the sector, so early detection and a timely response are crucial. This can be enabled by monitoring the pathogen levels in aquaculture facilities. This paper describes a photonic biosensing platform based on silicon nitride waveguide technology with integrated active components, which could be used for such applications. Compared to the state of the art, the current system presents improvements in terms of miniaturization of the Photonic Integrated Circuit (PIC) and the development of wafer-level processes for hybrid integration of active components and for material-selective chemical and biological surface modification. Furthermore, scalable processes for integrating the PIC in a microfluidic cartridge were developed, as well as a prototype desktop readout instrument. Three bacterial aquaculture pathogens (Aeromonas salmonicida, Vagococcus salmoninarum, and Yersinia ruckeri) were selected for assay development. DNA biomarkers were identified, corresponding primer-probe sets designed, and qPCR assays developed. The biomarker for Aeromonas was also detected using the hybrid PIC platform. This is the first successful demonstration of biosensing on the hybrid PIC platform.

(Invited) Advanced III-V-on-Si Heterogeneously Integrated Platforms for Next Generation Silicon Photonics Integrated Circuits

The field of Silicon Photonics has experienced a solid and continuous progress over the last few years, gaining in technological maturity, design tools, and new methods [1]. The deployment of low-cost, compact, and power-efficient photonic circuits with a high wafer yield and robustness stands as one of the fundamental pillars that sustain such progress [2]. Presently, photonic circuit technology has diversified its number of available platforms. Despite the fact that indium phosphide (InP) [3] and silicon-on-insulator (SOI) platforms are still considered as the workhorses of integrated photonics in terms of maturity and deployment of active (InP) and passive (SOI) components, other alternatives such as germanium-on-silicon [4], silicon nitride-on-insulator [2] or hybrid solutions combining different functional materials with Si are gaining momentum [5]. A representative example is the heterogeneous III-V/Si platform [6], which has been used to develop compact photonic circuits with on-chip gain. Contrarily to the hybrid integration, the III-V-on-Si heterogeneous integration avoids the constraints of chip-to-chip alignment while enabling the simultaneous integration of hundreds of III-V gain chips in a scalable fashion. Still, the integration methods of such III-V materials on silicon need to be improved to attain the maturity level of the monolithic III-V platform, which benefits from a complete palette of technological solutions not yet available in the III-V-on-Si heterogeneous integration. More recently, an advanced heterogeneous scheme based on wafer-seed-bonding and epitaxial regrowth has emerged [7]–[9]. The ambition is to create a generic integration scheme combining the best offered by the III-V and the Si-photonics platforms. The regrowth capability gives access to the large epitaxial toolkit available in the conventional InP monolithic platform, where several epitaxial steps are often implemented [10][11]. To cite some of them, the epitaxial regrowth of III-V materials to bury III-V lasers bonded onto silicon are object of intense research nowadays to overcome the thermally inefficient buried oxide [12].In this paper, we will review the advances on III-V-on-Si heterogeneous integration through the implementation of several key demonstrators and building blocks for silicon photonics, including on-chip semiconductor optical amplifiers, lasers and electro-absorption modulators. We will discuss the progress and benefits of the direct seed bonding and regrowth as well as new device designs to improve the performance.

Navigating the Fractional Calcium Dynamics of Orai Mechanism in Polar Dimensions.

Calcium plays a crucial role as a second messenger in neuronal signal transduction pathways. The influx of calcium ions through various physicochemical gating channels activates neuronal calcium signaling. The Endoplasmic Reticulum (ER) is a significant intracellular structure that sequesters calcium and controls signaling through SERCA, IPR, and leak channel mechanisms. Disruption of calcium dynamics can trigger intrinsic dyshomeostasis, cell damage, and apoptosis. The present study articulates a Caputo fractional time derivative in the polar coordinate dimensions to investigate the role of nonlocal calcium-free ions in the neuron through the Orai channel, and ER fluxes, incorporating various physiological parameters. The solution was obtained through the hybrid integral transform technique for analytical form. The closed form was generated using Green's function in terms of Mainardi and Wright's functions. Our simulation uncovered the calcium concentration bandwidth of interaction with different neuronal parameters. Parameters and calcium ion synergy show normal and Alzheimer's disease-impacted interaction through different illustrations. Our simulation reveals that S100B and BAPTA have significant calcium-controlling behavior.

Generalised Neuronal Calcium Dynamics of Membrane and ER in the Polar Dimension.

Calcium ions are the second messenger playing as regulators for various cellular activities. Its spatiotemporal control is critical for various brain functions, including neuroplasticity, apoptosis, and cell death. The Endoplasmic Reticulum (ER) plays an important role in determining these spatiotemporal calcium dynamics. Stromal interaction molecule (STIM) - Orai channel on the membrane generates additional calcium flow, whereas other membrane fluxes contribute to cytosolic flux. Due to their anomalous character, we used the Caputo fractional differential operator to mimic these interactions in polar coordinates. Solutions were generated using hybrid integral transform methods to control the analytical approach. Using Green's function yielded a closed-form solution for Mittag-Leffler-type functions. This work emphasizes the significant relationship between calcium and various buffer levels in neurons. The differential transition simulation of a time derivative with space across different parameters indicated a decrease in calcium concentration. Anomalously low buffer levels exhibited the impact of Alzheimer's disease on calcium higher concentration, leading to the death of neurons. Additionally, the research introduces a method involving S100B, BAPTA, and calmodulin buffers to uphold optimal calcium levels within the neuronal cytosol. The applicability of this model with different buffer properties and parameters and memory impacts the calcium concentration with the neurological disorder.

A Hybrid Integration Method Based on SMC-PHD-TBD for Multiple High-Speed and Highly Maneuverable Targets in Ubiquitous Radar

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method’s capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system.