Hybrid Integration Research Articles

Electrodeposited Nanowires for High Density Interconnects of GaN-Based MicroLEDs

For next-generation applications, fully controllable gallium nitride (GaN)-based micro light emitting diode (microLED) arrays will play a crucial role. These feature high pixel densities, high intensities, and high efficiencies. Well-established top-down etching processing schemes of pristine LED wafers allow for an easily accessible and scalable fabrication approach for microLED arrays down to the single digit µm dimensions with dies featuring several 100,000 on a square centimeter. At this scale, classical bonding approaches like wire bonding fail to harvest the true potential of such an array due to inherit technological restrictions. To allow for the individual controllability of every pixel a highly scalable integration approach to a CMOS backplane is necessary. Established processes for the realization of such an integration often feature major drawbacks. Indium bumps deposited via thermal evaporation generally fulfill the scale requirements but fail to adapt to the unique challenges of such hybrid integration. High non-planarity of the substrate, high wafer bow, and mismatching thermal expansion coefficients can lead to diminishing bonding yields and hence functioning microLEDs during the high-temperature bonding step necessary for indium bumps. Additionally, Indium is an inferior electrical and thermal conductor to other metals and limits the active performance of the device while also restricting its usage area due to its low melting point. In the currently ongoing EU Project SMILE, we are developing GaN-based microLED arrays up to 512 x 512 pixels and thoroughly examine established bonding technologies as well as an in-house developed metal nanowire approach. The necessary perquisites for electrochemical deposition, namely the conductive seed and the removal thereof can easily be integrated into the device’s processing scheme. Nanowires are electrochemically deposited out of a liquid solution at room temperature onto the microLED array and the CMOS backplane. The direction, length, and diameter of the nanowires can easily be controlled to accustom different proportions and bonding geometries. Inline monitoring via the recorded amperogram allows for the differentiation of the individual stages of nanowire growth during the electrochemical deposition. Mechanically stable and electrically sufficient interconnects can then be realized by pressing the nanowires of both substrates into each other. The high aspect ratio of the wires results in a high surface area and allows for nanowires of both substrates to intertwine forming stable connections with large contact surfaces at low pressures without any additional heating steps necessary. Due to the variability of the wire length and ongoing intertwining during the bonding, non-planarities and wafer bow can be accommodated. This flexible bonding behavior is a unique advantage of bonds based on metal nanowires. The junctions approach the conductivity of bulk material and are crucial for the thermal management of the microLED array. The final LED device generates a substantial amount of heat, which cannot be cooled meaningfully via convection to the surrounding air or by its radiation. Consequently, a capable thermal conduction towards the silicon chip is necessary for heat management. Here the metal nanowires play a crucial role in providing this thermal connection via the bonding interface, which potentially allows for improved device performance over other bonding technologies.

Hybrid machine learning models for aboveground biomass estimations

Forest biomass provides a quantitative assessment for carbon stock marketing on a national or regional scale. Some countries have committed to net zero carbon emissions, so proper biomass estimations are essential. This study investigates the uses of machine learning (LightGBM, XGBoost), in which hyperparameters were tuned by Bayesian-based Optimisers and a novel Tasmanian Devil Optimisation algorithm for estimates of aboveground biomass (AGB) using Sentinel 1A, Landsat images, and ground survey data. A province in the northern part of Vietnam was selected as a case study since the change in land cover has been considered crucial. The models were optimized/trained and validated using statistical indicators, namely, root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The trained models were further explained using SHAP values to understand better how they perform and the contribution of each feature to the overall estimates. The results showed that the three indicators of the proposed model were statistically better than those of the reference methods. Specifically, the hybrid model ended up at RMSE ∼13.87, MAE ∼ 10.62, and R2 ∼ 0.79 for the estimation of AGB. Based on the experience, such hybrid integration can be recommended as an alternative solution for biomass estimation. In a broader context, the fast growth of machine learning and optimization algorithms has created new scientifically sound solutions for a better analysis of forest cover.

Design, development, integration and evaluation of hybrid fuel cell power systems for an unmanned water surface vehicle

When fuel cells are used to power mobile applications, such a vehicles, hybridization with batteries is normally required. Depending on the electronic coupling between the energy sources the power plants can have passive or active configurations. Hybrid fuel cell-battery power plants with active power control flow have some advantages. For example, they can decrease the total energy losses, while improving the fuel cell performance, extending its lifetime. Power plants with DC/DC converters show low specific energy ratios, but with a superior energy management. In the present research, the hybrid power plant for an unmanned aquatic surface vehicle (USV) based on a PEM fuel cell and a Li-ion battery is developed. Active (with DC–DC converters) or passive architectures are analyzed by numerical simulations and experimental tests. Good results are obtained for the active power plant, where the peak power demands are managed by the battery pack while the fuel cell power remains constant thanks to the DC-converter control. The study shows that a simple control algorithm (no optimal) can help to extend the USV autonomy above 12h in calm waters with a specific energy of 85.6Whkg-1.

Параболічні крайові задачі математичної фізики в кусково-однорідному клиновидному циліндрично-круговому півпросторі з порожниною

The unique exact analytical solutions of parabolic boundary value problems of mathematical physics in piecewise homogeneous by the radial variable r wedge-shaped by the angular variable φ cylindrical-circular half-space with a cavity were constructed at first time by the method of classical integral and hybrid integral transforms in combination with the method of main solutions (matrices of influence and Green matrices) in the proposed article. The cases of assigning on the verge of the wedge the boundary conditions of the 1st kind (Dirichlet) and the 2nd kind (Neumann) and their possible combinations (Dirichlet – Neumann, Neumann – Dirichlet) are considered. Finite integral Fourier transform by an angular variable, a finite integral Fourier transform on the Cartesian semiaxis (0; +∞) by an applicative variable z and a Weber hybrid integral transform type on the polar axis (R0; +∞) with n points of conjugation by a radial variable were used to construct solutions of investigated boundary value problems. The consistent application of integral transforms by geometric variables allows us to reduce the three-dimensional initial boundary-value problems of conjugation to the Cauchy problem for a regular linear inhomogeneous 1st order differential equation whose unique solution is written in a closed form. The consistent application of inverse integral transforms to the obtained solution in the space of images restores the solutions of the considered parabolic boundary value problems through their integral image in an explicit form in the space of the originals. At the same time, the main solutions to the problems were obtained in an explicit form.

Spaceborne distributed aperture radar maneuvering target detection approach with space-time 2D hybrid integration technique

Moving target detection in spaceborne distributed aperture radar (SBDAR) is a challenging task in civilian and military fields. The common issues are that the current methods suffer from the large range cell migration (RCM) and time-varying Doppler frequency modulation (DFM) problems in space-time two-dimensional (2D) domain, which is caused by distributed aperture radar detection configuration and the maneuvering characteristics of moving targets. To address these issues, a novel SBDAR moving target detection approach via space–time hybrid integration (STHI) is proposed in this paper. Compared with single or bistatic radar mode, the utilization of multiple satellites provides an improvement in moving target detection performance, together with the capabilities of localization and parameters estimation. The proposed method mainly includes the following three steps. First, the geometric and signal models of an observed maneuvering target are accurately established in a SBDAR framework, where the space-time 2D RCM and DFM are analyzed. Based on that, the second-order Keystone transform (SKT), a novel range frequency reversal process (NRFRP) and a modified scaled Fourier transform (MSCFT) are developed to correct the RCM and DFM, achieving an efficient time dimension coherent integration for maneuvering target. After that, by dividing the radar detection area, the spatial projection method is adopted to integrate the moving target energy in space dimension. Finally, the simulation results verify the effectiveness and advantages of the proposed method.

Microfiber knot resonator augmented quartz-enhanced photoacoustic spectroscopy

In this work, a microfiber knot resonator augmented quartz-enhanced photoacoustic spectroscopy (MKR-QEPAS) was developed for the analysis of trace gases. In the proof-of-concept experiment, a 1.39 μm laser diode was utilized for the detection of H2O molecules. Based on the H2O absorption line, the optical fiber-based MKR was meticulously designed to modulate the transmission spectrum, thereby inducing constructive interference with the excitation laser wavelength. The MKR was integrated with a clamp-type tuning fork, leading to a pronounced evanescent resonance enhancement. Consequently, the detection sensitivity of the MKR-QEPAS was improved by approximately one order of magnitude. The micro ring resonator is posited to hold significant promise for hybrid integration in QEPAS on-chip sensors.

Hybrid integrators with predictive overload estimation for analog computers and continuous-time ΔΣ modulators

Abstract. Continuous-time integrators are a central component in ΔΣ modulators, in analog computers, and general analog signal processing. If several integrators are interconnected, scaling plays an important role: In analog computers, scaling is performed with respect to the machine unit (MU). In ΔΣ modulators, scaling is performed in such a way that at maximum input signal the allowable dynamic range of no integrator is exceeded. In both cases the scaling is a compromise limiting the dynamic range. For analog computers, it was proposed early on to extend the dynamic range by hybrid integrators. Here, an analog range overflow is processed digitally and the analog integrator is reduced to its permissible operating range within the machine unit interval. While in earlier proposals for hybrid integrators only the subsequent integrator stage processes the overflow and works with reduced analog values, our hybrid integrator can process the overflow directly, with the analog reset process being continuous-time. In the case of highly dynamical input signals and transients, analog overload handling is further improved by a prediction of the overload that includes the currently applied input signal in the calculation. For example, with continuous-time ΔΣ modulators, overload of the analog integrator can be reliably avoided.

Telecom-band quantum dot technologies for long-distance quantum networks.

A future quantum internet is expected to generate, distribute, store and process quantum bits (qubits) over the world by linking different quantum nodes via quantum states of light. To facilitate long-haul operations, quantum repeaters must operate at telecom wavelengths to take advantage of both the low-loss optical fibre network and the established technologies of modern optical communications. Semiconductor quantum dots have thus far shown exceptional performance as key elements for quantum repeaters, such as quantum light sources and spin-photon interfaces, but only in the near-infrared regime. Therefore, the development of high-performance telecom-band quantum dot devices is highly desirable for a future solid-state quantum internet based on fibre networks. In this Review, we present the physics and technological developments towards epitaxial quantum dot devices emitting in the telecom O- and C-bands for quantum networks, considering both advanced epitaxial growth for direct telecom emission and quantum frequency conversion for telecom-band down-conversion of near-infrared quantum dot devices. We also discuss the challenges and opportunities for future realization of telecom quantum dot devices with improved performance and expanded functionality through hybrid integration.

Optimal integration of hybrid pumped storage hydropower toward energy transition

This study explores the advantages of combining variable renewable energy sources like solar and wind with a pumped storage hydroelectric (PSH) system for grid integration. The hybrid modeling systems considered in this study consist of four distinct schemes and seasons to ensure their adaptability to real-world conditions. Each scheme is defined by specific rules and algorithms, including those without PSH, with PSH, with PSH adjustments, and with an optimized schedule for the PSH storage system. The last scheme of daily simulation with an optimization solver is used to optimize the integration of PSH with a hybrid power system that uses solar and wind energy as primary renewable sources by minimizing the daily operating costs. The optimal value is derived from the minimized operating costs and incoming and outgoing energy accumulation. The dispatch system includes load demand satisfaction considering the intermittent nature of solar and wind sources and demand fluctuations presented with a hybrid system of PV solar, wind turbines, PSH, and other power generators as backup. Limitations of the study include considering various factors influencing the integration of a PSH unit, such as different ecological and geographical conditions, distinct atmospheric data, and varying load and demand patterns, which may result in different outcomes in different situations. The results show that using the developed model to optimally schedule the integration of PSH with renewables and the hybrid system in each season provides significant cost savings and CO₂ emissions reductions while meeting the same load patterns. Namely, it can be inferred from the tested schemes that PSH plays a crucial role in facilitating the transition to sustainable energy sources. This is evident through project financing results, which indicate a favorable positive Net Present Value (NPV) and a relatively short payback period of 5 years. Furthermore, Scheme 4 demonstrates the potential for a substantial 84 % reduction in CO₂ emissions during the summer season, and daily costs can be significantly lowered by over 90 % across all seasons. Overall, two simplified net present value and payback period estimation models have shown feasible project financing for a simulation period of 25 years with an interest rate of 10 % for the combined PSH and hybrid system.

Port supply chain integration under mixed ownership

In the port supply chain, vertical integration between ports and shipping lines is no longer confined to private integration, but is more likely performed as a hybrid integration between public ports and private shipping lines. In this study, we consider port supply chain integration between a public or private port and its shipping line, which provides differentiated services in the shipping market, and investigate shipping lines’ competition contracts under mixed ownership. We show that if shipping lines choose a volume contract, double vertical integration between two ports and their corresponding shipping lines yields the highest welfare; however, if shipping lines choose a price contract, single private integration can yield the highest welfare. We also endogenize the supply chain integration and find that a double integration-and then-price contract is socially beneficial only when the private share of a mixed merger is low while the shipping service substitutability is neither high nor low. Our findings suggest that government ownership of a public port can serve as an efficient tool for port policy in the context of port supply chain integration.

Photorefraction‐Assisted Self‐Emergence of Dissipative Kerr Solitons

AbstractGenerated in high‐Q optical microresonators, dissipative Kerr soliton microcombs constitute broadband optical frequency combs with chip sizes and repetition rates in the microwave to millimeter‐wave range. For frequency metrology applications such as spectroscopy, optical atomic clocks, and frequency synthesizers, octave‐spanning soliton microcombs generated in dispersion‐optimized microresonators are required, which allow self‐referencing for full frequency stabilization. In addition, field‐deployable applications require the generation of such soliton microcombs to be simple, deterministic, and reproducible. Here, a novel scheme to generate self‐emerging solitons in integrated lithium‐niobate microresonators is demonstrated. The single soliton features a broadband spectral bandwidth with dual dispersive waves, allowing 2f–3f self‐referencing. Via harnessing the photorefractive effect of lithium niobate to significantly extend the soliton existence range, a spontaneous yet deterministic single‐soliton formation is observed. The soliton is immune to external perturbation and can operate continuously for over 13 h without active feedback control. Finally, via integration with a pre‐programmed distributed feedback (DFB) laser, turnkey soliton generation is demonstrated. With further improvement of microresonator Q and hybrid integration with chip‐scale laser chips, compact soliton microcomb devices with electronic actuation can be created, which can become central elements for future LiDAR, microwave photonics, and optical telecommunications.

Sub-ppm Methane Detection with Mid-Infrared Slot Waveguides.

Hybrid integration of photonic chips with electronic and micromechanical circuits is projected to bring about miniature, but still highly accurate and reliable, laser spectroscopic sensors for both climate research and industrial applications. However, the sensitivity of chip-scale devices has been limited by immature and lossy photonic waveguides, weak light-analyte interaction, and etalon effects from chip facets and defects. Addressing these challenges, we present a nanophotonic waveguide for methane detection at 3270.4 nm delivering a limit of detection of 0.3 ppm, over 2 orders of magnitude lower than the state-of-the-art of on-chip spectroscopy. We achieved this result with a Si slot waveguide designed to maximize the light-analyte interaction, while special double-tip fork couplers at waveguide facets suppress spurious etalon fringes. We also study and discuss the effect of adsorbed humidity on the performance of mid-infrared waveguides around 3 μm, which has been repeatedly overlooked in previous reports.

Chip‐Based Microwave Photonic Payload Repeater for High Throughput Satellites

AbstractHigh‐throughput satellite (HTS) is an ideal way to realize cross‐regional massive, multifaceted digital exchange services, and it requires a signal processing module that can be massively multiplexed and has high flexibility. Due to the limitations of the frequency characteristics, microwave integrated circuits are difficult to meet this requirement. One solution to this problem is photonic integrated circuits (PICs). However, full‐size PIC satellite payloads containing main optoelectronic components are extremely challenging to implement on monolithic or hybrid integrated platforms. Here, the study demonstrates a hybrid integrated on‐chip microwave‐photonic satellite repeater with large‐scale multiplexing potential and high flexibility. This is a demonstration of a hybrid integration of a InP/Si3N4 external cavity laser, arrayed InP modulators, and semiconductor optical amplifiers (SOAs), as well as multifunctional Si3N4 signal processors, to fulfill a 1 × 4 Ka‐band repeater module with on‐chip arrayed frequency down‐conversion and outstanding narrowband photonic channelization. When combined with the full‐chip photonic RF repeater, broadband, highly integrated, and cost‐effective communications satellite payloads will become realizable more quickly in the near future.

Advances in Biocarbon and Soft Material Assembly for Enthalpy Storage: Fundamentals, Mechanisms, and Multimodal Applications.

High-value-added biomass materials like biocarbon are being actively pursued integrating them with soft materials in a broad range of advanced renewable energy technologies owing to their advantages, such as lightweight, relatively low-cost, diverse structural engineering applications, and high energy storage potential. Consequently, the hybrid integration of soft and biomass-derived materials shall store energy to mitigate intermittency issues, primarily through enthalpy storage during phase change. This paper introduces the recent advances in the development of natural biomaterial-derived carbon materials in soft material assembly and its applications in multidirectional renewable energy storage. Various emerging biocarbon materials (biochar, carbon fiber, graphene, nanoporous carbon nanosheets (2D), and carbon aerogel) with intrinsic structures and engineered designs for enhanced enthalpy storage and multimodal applications are discussed. The fundamental design approaches, working mechanisms, and feature applications, such as including thermal management and electromagnetic interference shielding, sensors, flexible electronics and transparent nanopaper, and environmental applications of biocarbon-based soft material composites are highlighted. Furthermore, the challenges and potential opportunities of biocarbon-based composites are identified, and prospects in biomaterial-based soft materials composites are presented.

Enhancing Battery State of Charge Estimation through Hybrid Integration of Barnacles Mating Optimizer with Deep Learning

The precise determination of battery state of charge (SoC) holds paramount significance and has garnered considerable attention across diverse sectors, including academia. Accurate knowledge of the SoC percentage offers numerous advantages, ranging from optimizing travel planning to enhancing the efficiency and reliability of electric vehicle operations through effective battery management systems. In response to the growing importance of SoC estimation, this study introduces a hybrid approach called the Barnacles Mating Optimizer with Deep Learning (BMO-DL) for SoC of Nissan Leaf batteries. The conventional methods for SoC estimation often suffer from limitations in accuracy and robustness, leading to suboptimal EV performance and battery management. In contrast, BMO-DL leverages the power of BMO algorithm to fine-tune the hyperparameters of DL, which is subsequently employed for the actual estimation. This synergistic combination enhances the accuracy and reliability of SoC estimation. The estimation model takes three inputs: voltage, current and conducted charge to generate a single output, the SoC percentage. he study's findings underscore the superiority of BMO-DL by revealing its capability to achieve significantly better results compared to the other benchmarking methods identified. Notably, BMO-DL exhibits significantly lower error rates when compared to competing algorithms, thereby reinforcing its potential to advance the efficiency and reliability of electric vehicle operations while addressing the critical challenge of SoC prediction.

Ultrafast mode-locked laser in nanophotonic lithium niobate.

Mode-locked lasers (MLLs) generate ultrashort pulses with peak powers substantially exceeding their average powers. However, integrated MLLs that drive ultrafast nanophotonic circuits have remained elusive because of their typically low peak powers, lack of controllability, and challenges when integrating with nanophotonic platforms. In this work, we demonstrate an electrically pumped actively MLL in nanophotonic lithium niobate based on its hybrid integration with a III-V semiconductor optical amplifier. Our MLL generates [Formula: see text]4.8-ps optical pulses around 1065 nm at a repetition rate of ∼10 GHz, with energies exceeding 2.6 pJ and peak powers beyond 0.5 W. The repetition rate and the carrier-envelope offset frequency of the output can be controlled in a wide range by using the driving frequency and the pump current, providing a route for fully stabilized on-chip frequency combs.

Hybrid Integration of Au Nanoplates and Ag Nanoparticles with Controlled Nanogaps via Cu2O‐mediated Galvanic Replacement Reaction: Plasmonic Catalysis and Photothermal Conversion

AbstractRational assembling of different noble metal nanocrystals with controlled nanogaps in one entity allows the efficient coupling of electromagnetic fields of each component and versatile manipulation over the overall physiochemical properties. In the present study, we report a stepwise synthetic strategy to hybridlike assemble Au nanoplate and Ag nanoparticles without direct physical contact. Particularly, the success of current work relies on the conformal coating of Cu2O over the Au nanoplate as an intermediate layer, followed by the in situ formation of Ag nanoparticles via the galvanic replacement reaction between Cu2O and Ag+. With the anchoring of Ag nanoparticles over the Cu2O‐wrapped Au nanoplate, the resulting hybrid nanostructures exhibit noticeable multiple plasmonic absorptions across the UV‐vis‐NIR region. The potential use as the plasmonic catalyst for current Au@Cu2O‐Ag hybrid products is validated using reduction of 4‐nitrophenol as the model reaction, where the catalytic performance is greatly improved by the UV‐vis light irradiation. They also exhibit satisfactory photothermal conversion performance under the irradiation of Xe lamp. The current work provides a niche strategy to create noble‐metal hybrid nanostructures with controlled interparticle distance and tunable plasmonic properties, could be extended to other noble metal or alloys.

A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks

Simulating the evolution of the gravitational N-body problem becomes extremely computationally expensive as N increases since the problem complexity scales quadratically with the number of bodies. In order to alleviate this problem, we study the use of Artificial Neural Networks (ANNs) to replace expensive parts of the integration of planetary systems. Neural networks that include physical knowledge have rapidly grown in popularity in the last few years, although few attempts have been made to use them to speed up the simulation of the motion of celestial bodies. For this purpose, we study the advantages and limitations of using Hamiltonian Neural Networks to replace computationally expensive parts of the numerical simulation of planetary systems, focusing on realistic configurations found in astrophysics. We compare the results of the numerical integration of a planetary system with asteroids with those obtained by a Hamiltonian Neural Network and a conventional Deep Neural Network, with special attention to understanding the challenges of this specific problem. Due to the non-linear nature of the gravitational equations of motion, errors in the integration propagate, which may lead to divergence from the reference solution. To increase the robustness of a method that uses neural networks, we propose a hybrid integrator that evaluates the prediction of the network and replaces it with the numerical solution if considered inaccurate.Hamiltonian Neural Networks can make predictions that resemble the behavior of symplectic integrators but are challenging to train and in our case fail when the inputs differ ∼7 orders of magnitude. In contrast, Deep Neural Networks are easy to train but fail to conserve energy, leading to fast divergence from the reference solution. The hybrid integrator designed to include the neural networks increases the reliability of the method and prevents large energy errors without increasing the computing cost significantly. For the problem at hand, the use of neural networks results in faster simulations when the number of asteroids is ≳70.

Silicon-Organic Hybrid Electro-Optic Modulator and Microwave Photonics Signal Processing Applications.

Electro-optic modulator (EOM) is one of the key devices of high-speed optical fiber communication systems and ultra-wideband microwave photonic systems. Silicon-organic hybrid (SOH) integration platform combines the advantages of silicon photonics and organic materials, providing a high electro-optic effect and compact structure for photonic integrated devices. In this paper, we present an SOH-integrated EOM with comprehensive investigation of EOM structure design, silicon waveguide fabrication with Slot structure, on-chip poling of organic electro-optic material, and characterization of EO modulation response. The SOH-integrated EOM is measured with 3 dB bandwidth of over 50 GHz and half-wave voltage length product of 0.26 V·cm. Furthermore, we demonstrate a microwave photonics phase shifter by using the fabricated SOH-integrated dual parallel Mach-Zehnder modulator. The phase shift range of 410° is completed from 8 GHz to 26 GHz with a power consumption of less than 38 mW.

An efficient model for vehicle-track-soil dynamic interaction based on Green's function, cyclic calculation and multi-time-step solution methods

To meet the demand for dynamic analysis of railway engineering structures accurately, the numerical model considering the coupled vibrations of the vehicle, track and substructure becomes a research tendency. However, it generates low-efficiency risk due to high degrees of freedom of the model. In this paper, a hybrid integration method based on the implicit scheme and Green's function is introduced and combined with the cyclic calculation method and the multi-time-step solution method to optimize the dynamic procedure for high-efficient solution of vehicle-track-soil (VTS) dynamic interaction. The VTS system is divided into two sub-models: vehicle-rail sub-model and the track slab-soil sub-model. The compatibility between two sub-models is maintained by the equilibrium mechanism of the fasteners’ interaction forces. Through detailed validation, discussion and numerical studies, the feasibility and efficiency of the proposed model combing the hybrid integration method and two optimized dynamic solution strategies are fully demonstrated in the dynamic assessment of VTS dynamic interaction.