The Roadmap of 2D Materials and Devices Toward Chips

Due to the constraints imposed by physical effects and performance degra certain limitations in sustaining the advancement of Moore’s law.Two-dimensional(2D)materials have emerged as highly promising candidates for the post-Moore era,offering significant potential in domains such as integrated circuits and next-generation computing.Here,in this review,the progress of 2D semiconductors in process engineering and various electronic applications are summarized.A careful introduction of material synthesis,transistor engineering focused on device configuration,dielectric engineering,contact engineering,and material integration are given first.Then 2D transistors for certain electronic applications including digital and analog circuits,heterogeneous integration chips,and sensing circuits are discussed.Moreover,several promising applications(artificial intelligence chips and quantum chips)based on specific mechanism devices are introduced.Finally,the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed,and potential development pathways or roadmaps are further speculated and outlooked.

Targeted regeneration and upcycling of spent graphite by defect‐driven tin nucleation

The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable development.Compared with the degraded cathode,spent anode graphite often has a relatively intact structure with few defects after long cycling.Yet,most spent graphite is simply burned or discarded due to its limited value and inferior performance on using conventional recycling methods that are complex,have low efficiency,and fail in performance restoration.Herein,we propose a fast,efficient,and“intelligent”strategy to regenerate and upcycle spent graphite based on defect‐driven targeted remediation.Using Sn as a nanoscale healant,we used rapid heating(~50 ms)to enable dynamic Sn droplets to automatically nucleate around the surface defects on the graphite upon cooling owing to strong binding to the defects(~5.84 eV/atom),thus simultaneously achieving Sn dispersion and graphite remediation.As a result,the regenerated graphite showed enhanced capacity and cycle stability(458.9 mAh g^(−1) at 0.2 A g^(−1) after 100 cycles),superior to those of commercial graphite.Benefiting from the self‐adaption of Sn dispersion,spent graphite with different degrees of defects can be regenerated to similar structures and performance.EverBatt analysis indicates that targeted regeneration and upcycling have significantly lower energy consumption(~99%reduction)and near‐zero CO_(2) emission,and yield much higher profit than hydrometallurgy,which opens a new avenue for direct upcycling of spend graphite in an efficient,green,and profitable manner for sustainable battery manufacture.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution(LTE)-Advanced and 5G New Radio(NR)mobile networks.However,these techniques introduce Passive Inter-Modulation(PIM)interference in Frequency-Division Duplexing(FDD)systems.In this paper,a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals,thereby achieving effective PIM Cancellation(PIMC)in multi-band scenarios.In the model,transmitted signals are independently processed to simulate Inter-Modulation Distortions(IMDs)and Cross-Modulation Distortions(CMDs).Furthermore,the Finite Impulse Response(FIR)filter,basis function generation,and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios.Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC,achieving up to 19 dB in PIMC performance.Compared to other models,this approach offers superior PIMC performance,exceeding them by more than 5 dB in high transmitting power scenarios.Additionally,its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

Task Offloading and Resource Allocation in NOMA-VEC:A Multi-Agent Deep Graph Reinforcement Learning Algorithm

Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.

Hybrid bonding of GaAs and Si wafers at low temperature by Ar plasma activation

High-quality bonding of 4-inch GaAs and Si is achieved using plasma-activated bonding technology.The influence of Ar plasma activation on surface morphology is discussed.When the annealing temperature is 300℃,the bonding strength reaches a maximum of 6.2 MPa.In addition,a thermal stress model for GaAs/Si wafers is established based on finite element analysis to obtain the distribution of equivalent stress and deformation variables at different temperatures.The shape varia-tion of the wafer is directly proportional to the annealing temperature.At an annealing temperature of 400℃,the maximum protrusion of 4 inches GaAs/Si wafers is 3.6 mm.The interface of GaAs/Si wafers is observed to be dense and defect-free using a transmission electron microscope.The characterization of interface elements by X-ray energy dispersion spectroscopy indi-cates that the elements at the interface undergo mutual diffusion,which is beneficial for improving the bonding strength of the interface.There is an amorphous transition layer with a thickness of about 5 nm at the bonding interface.The preparation of Si-based GaAs heterojunctions can enrich the types of materials required for the development of integrated circuits,improve the performance of materials and devices,and promote the development of microelectronics technology.

Atomic-level quantitative analysis of electronic functional materials by aberration-corrected STEM

The stable sub-angstrom resolution of the aberration-corrected scanning transmission electron microscope(ACSTEM)makes it an advanced and practical characterization technique for all materials.Owing to the prosperous advancement in computational technology,specialized software and programs have emerged as potent facilitators across the entirety of electron microscopy characterization process.Utilizing advanced image processing algorithms promotes the rectification of image distortions,concurrently elevating the overall image quality to superior standards.Extracting high-resolution,pixel-level discrete information and converting it into atomic-scale,followed by performing statistical calculations on the physical matters of interest through quantitative analysis,represent an effective strategy to maximize the value of electron microscope images.The efficacious utilization of quantitative analysis of electron microscope images has become a progressively prominent consideration for materials scientists and electron microscopy researchers.This article offers a concise overview of the pivotal procedures in quantitative analysis and summarizes the computational methodologies involved from three perspectives:contrast,lattice and strain,as well as atomic displacements and polarization.It further elaborates on practical applications of these methods in electronic functional materials,notably in piezoelectrics/ferroelectrics and thermoelectrics.It emphasizes the indispensable role of quantitative analysis in fundamental theoretical research,elucidating the structure–property correlations in high-performance systems,and guiding synthesis strategies.

Wireless Information and Power Transfer in Underwater Acoustic Sensor Networks

Wireless information and power transfer(WIPT) enables simultaneously communications and sustainable power supplement without the erection of power supply lines and the replacement operation of the batteries for the terminals. The application of WIPT to the underwater acoustic sensor networks(UWASNs) not only retains the long range communication capabilities, but also provides an auxiliary and convenient energy supplement way for the terminal sensors, and thus is a promising scheme to solve the energy-limited problem for the UWASNs. In this paper, we propose the integration of WIPT into the UWASNs and provide an overview on various enabling techniques for the WIPT based UWASNs(WIPT-UWASNs) as well as pointing out future research challenges and opportunities for WIPT-UWASNs.

Chlorine-Substituent Regulation in Dopant-Free Small-Molecule Hole-Transport Materials Improves the Effi ciency and Stability of Inverted Perovskite Solar Cells

Although doped hole-transport materials(HTMs)off er an effi ciency benefi t for perovskite solar cells(PSCs),they inevi-tably diminish the stability.Here,we describe the use of various chlorinated small molecules,specifi cally fl uorenone-triphenylamine(FO-TPA)-x-Cl[x=para,meta,and ortho(p,m,and o)],with diff erent chlorine-substituent positions,as dopant-free HTMs for PSCs.These chlorinated molecules feature a symmetrical donor-acceptor-donor structure and ideal intramolecular charge transfer properties,allowing for self-doping and the establishment of built-in potentials for improving charge extraction.Highly effi cient hole-transfer interfaces are constructed between perovskites and these HTMs by strategi-cally modifying the chlorine substitution.Thus,the chlorinated HTM-derived inverted PSCs exhibited superior effi ciencies and air stabilities.Importantly,the dopant-free HTM FO-TPA-o-Cl not only attains a power conversion effi ciency of 20.82% but also demonstrates exceptional stability,retaining 93.8%of its initial effi ciency even after a 30-day aging test conducted under ambient air conditions in PSCs without encapsulation.These fi ndings underscore the critical role of chlorine-substituent regulation in HTMs in ensuring the formation and maintenance of effi cient and stable PSCs.

Status-Aware Resource Adaptation in Information-Centric and Software-Defined Network

In recent years, realising a ＂clean- state＂ design for the future Internet has become an important research focus. An architecture com- bining an Information-Centric Network （ICN） and Sottware-Defmed Network （SDN） （IC-SDN） has gradually attracted more attention. How- ever, the existing studies regarding IC-SDN still lack support in terms of the ＂network status awareness＂ function, resulting in unreasonable resource allocation. In this paper, we propose a new status-aware resource adaptation sche- me, i.e. a status-aware module is embedded into basic elements （Forwarding Node （FN） and Resource adaption Manager （RM））. The FNs collect the network status dynamically for the controller to reallocate network resources accor- ding to the fluctuations in environmental con- ditions. Simulation results show that, compared with the existing IC-SDN mechanism, the pro- posed scheme reduced the link bandwidth var- iance by 56% and the content delivery latency by 40%. The proof-of-concept implementation demonstrates the feasibility of our proposed sc- heme for small-scale deployment.

Flight parameter calculation method of multi-projectiles using temporal and spatial information constraint

The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

Competition between two kinds of information among random-walking individuals

A model is proposed to describe the competition between two kinds of information among N random-walking individuals in an L x L square, starting from a half-and-half mixture of two kinds of information. Individuals remain or change their information according to their neighbors＇ information. When the moving speed of individuals v is zero, the two kinds of information typically coexist, and the ratio between them increases with L and decreases with N. In the dynamic case （v 〉 0）, only one information eventually remains, and the time required for one information being left scales as Td -v^αL^β^γ.

Link Budget and Enhanced Communication Distance for Ambient Internet of Things

Backscatter communications will play an important role in connecting everything for beyond 5G(B5G)and 6G systems.One open challenge for backscatter communications is that the signals suffer a round-trip path loss so that the communication distance is short.In this paper,we first calculate the communication distance upper bounds for both uplink and downlink by measuring the tag sensitivity and reflection coefficient.It is found that the activation voltage of the envelope detection diode of the downlink tag is the main factor limiting the back-scatter communication distance.Based on this analysis,we then propose to implement a low-noise amplifier(LNA)module before the envelope detection at the tag to enhance the incident signal strength.Our experimental results on the hardware platform show that our method can increase the downlink communication range by nearly 20 m.

The Appeals,Dilemmas,and Pathways of Enabling Rural Teachers’Professional Development Through Emerging Technologies

With the rapid advancements in technology,especially in digitalization and intelligence,numerous modern technologies have poured into rural schools,effectively improving their informatization conditions.Nevertheless,these technologies remain detached from rural teachers,failing to significantly enhance the quality of education and teaching in rural areas.Rural education is a crucial aspect of ensuring balanced development in education.The question of how to enhance rural teachers’technological application abilities and fully leverage the positive role of technology in rural education and teaching has become a significant topic of current research on rural education issues.To better address this question,this study conducted a thorough examination of the specific appeals of rural teachers in the process of technology enablement.It was discovered that rural teachers generally face dilemmas such as insufficient technological application abilities,difficulties in obtaining quality teaching resources,and the lack of continuous technical support and update mechanisms.Based on these findings,specific pathways such as strengthening rural teacher training,optimizing the allocation of educational resources,and establishing mechanisms for continuous technical support and updates are proposed to aid in the high-quality development of rural education.

Structural,mechanical,and electronic properties of Zr-Te compounds from first-principles calculations

The first-principles calculations based on density functional theory are used to obtain structural,mechanical,and electronic properties of Zr-Te compounds.The optimized structural parameters are consistent with the available experimental data.The calculated mechanical properties and formation energy show that the Zr-Te compounds are all mechanically and thermodynamically stable.The bulk modulus B,shear modulus G,Young’s modulus E,Debye temperatureΘD,and sound velocity vm are listed,which are positively correlated with the increasing of atomic fraction of Zr.The behaviors of density of states of Zr-Te compounds are obtained.Furthermore,the electronic properties are discussed to clarify the bonding characteristics of compounds.The electronic characteristics demonstrate that the Zr-Te systems with different phases are both covalent and metallic.

Energy-Efficient Computation Offloading and Resource Allocation in Fog Computing for Internet of Everything

With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.

Cooperative adaptive bidirectional control of a train platoon for efficient utility and string stability

This paper proposes cooperative adaptive control schemes for a train platoon to improve efficient utility and guarantee string stability. The control schemes are developed based on a bidirectional strategy, i.e., the information of proximal（preceding and following） trains is used in the controller design. Based on available proximal information（prox-info） of location, speed, and acceleration, a direct adaptive control is designed to maintain the tracking interval at the minimum safe distance. Based on available prox-info of location, an observer-based adaptive control is designed to achieve the same target, which alleviates the requirements of equipped sensors to measure prox-info of speed and acceleration. The developed schemes are capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system, the string stability of train platoon is guaranteed on the basis of Lyapunov stability theorem. Numerical simulation results are presented to verify the effectiveness of the proposed control laws.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

Semantic segmentation method of road scene based on Deeplabv3+ and attention mechanism

In the study of automatic driving,understanding the road scene is a key to improve driving safety.The semantic segmentation method could divide the image into different areas associated with semantic categories in accordance with the pixel level,so as to help vehicles to perceive and obtain the surrounding road environment information,which would improve driving safety.Deeplabv3+is the current popular semantic segmentation model.There are phenomena that small targets are missed and similar objects are easily misjudged during its semantic segmentation tasks,which leads to rough segmentation boundary and reduces semantic accuracy.This study focuses on the issue,based on the Deeplabv3+network structure and combined with the attention mechanism,to increase the weight of the segmentation area,and then proposes an improved Deeplabv3+fusion attention mechanism for road scene semantic segmentation method.First,a group of parallel position attention module and channel attention module are introduced on the Deeplabv3+encoding end to capture more spatial context information and high-level semantic information.Then,an attention mechanism is introduced to restore the spatial detail information,and the data shall be normalized in order to accelerate the convergence speed of the model at the decoding end.The effects of model segmentation with different attention-introducing mechanisms are compared and tested on CamVid and Cityscapes datasets.The experimental results show that the mean Intersection over Unons of the improved model segmentation accuracies on the two datasets are boosted by 6.88%and 2.58%,respectively,which is better than using Deeplabv3+.This method does not significantly increase the amount of network calculation and complexity,and has a good balance of speed and accuracy.

Joint Timing and Frequency Synchronization for OFDM based on Fractional Fourier Transform

In this paper a joint timing and frequency synchronization method based on Fractional Fourier Transform （FIFT） is proposed for Orthogonal Frequency-Division Multiplexing （OFDM） system. The combination of two chirp signals with opposite chirp rates are used as the training signal, the received training signal with timing and frequency offset is transformed by FrFT and the two peaks representing two chirps in FrFT domain are detected, then the position coordinates of the two peaks are precisely corrected and substituted into an equation group to calculate timing and frequency offset simultaneously. This method only needs one FrFT calculation to implement synchronization, the computational complexity is equal to that of FFT and less than that of correlation or maximum likelihood calculation of existing methods, and estimation range of frequency offset is Large, greater than half the signal bandwidth, while the simulation results show that even at low SNR it can accurately estimate timing and frequency offset and the estimation error is less than that of existing methods.