First demonstration of a self-aligned p-channel GaN back gate injection transistor

In this study,we present the development of self-aligned p-channel Ga N back gate injection transistors(SA-BGITs)that exhibit a high ON-state current.This achievement is primarily attributed to the conductivity modulation effect of the 2-D electron gas(2DEG,the back gate)beneath the 2-D hole gas(2DHG)channel.SA-BGITs with a gate length of 1μm have achieved an impressive peak drain current(I_(D,MAX))of 9.9 m A/mm.The fabricated SA-BGITs also possess a threshold voltage of 0.15 V,an exceptionally minimal threshold hysteresis of 0.2 V,a high switching ratio of 10~7,and a reduced ON-resistance(RON)of 548Ω·mm.Additionally,the SA-BGITs exhibit a steep sub-threshold swing(SS)of 173 mV/dec,further highlighting their suitability for integration into Ga N logic circuits.

Upgrade and Renovation of Steel Billet Scalein front of High-speed Wire Furnace

This article briefly introduces the weighing device for steel billets in front of the heating furnace of the high-speed wire rod unit,and analyzes and summarizes the problems existing in the original weighing device for steel billets in production and use.Based on on-site installation conditions,design a new weighing method,match a large range weighing sensor,upgrade the automation control of the weighing device,and remotely transmit the billet weighing data to the MES system of the group.The automatic,stable,reliable,and accurate measurement of steel billet raw materials has been achieved,providing important guarantees for the accurate measurement of production line billet and product yield.

Positive Bias Temperature Instability and Hot Carrier Injection of Back Gate Ultra-thin-body In0.53Ga0.47As-on-Insulator n-Channel Metal-Oxide-Semiconductor Field-Effect Transistor

Ultra-thin-body （UTB） In0.53Ga0.47As-on-insulator （In0.53Ga0.47As-OI） structures with thicknesses of 8 and 15nm are realized by transferring epitaxially grown In0.53Ga0.47As layers to silicon substrates with 15-nmthick A12 03 as a buried oxide by using the direct wafer bonding method. Back gate n-channel metal-oxidesemiconductor field-effect transistors （nMOSFETs） are fabricated by using these In0.53Ga0.47As-OI structures with excellent electrical characteristics. Positive bias temperature instability （PBTI） and hot carrier injection （HCI） characterizations are performed for the In0.53Ga0.47As-OI nMOSFETs. It is confirmed that the In0.53Ga0.47 As-OI nMOSFETs with a thinner body thickness suffer from more severe degradations under both PBTI and HCr stresses. Moreover, the different evolutions of the threshold voltage and the saturation current of the UTB In0.53Ga0.47As-OI nMOSFETs may be due to the slow border traps.

Breakdown Voltage and Charge to Breakdown Investigation of Gate Oxide of 0.18μm Dual Gate CMOS Process with Different Measurement Methods

Breakdown voltage （Vbd） and charge to breakdown （Qbd） are two parameters often used to evaluate gate oxide reliability. In this paper,we investigate the effects of measurement methods on Vbd and Qbd of the gate oxide of a 0.18μm dual gate CMOS process. Voltage ramps （V-ramp） and current ramps （J-ramp） are used to evaluate gate oxide reliability. The thin and thick gate oxides are all evaluated in the accumulation condition. Our experimental results show that the measurement methods affect Vbd only slightly but affect Qbd seriously,as do the measurement conditions.This affects the I-t curves obtained with the J-ramp and V-ramp methods. From the I-t curve,it can be seen that Qbd obtained using a J-ramp is much bigger than that with a V-ramp. At the same time, the Weibull slopes of Qbd are definitely smaller than those of Vbd. This means that Vbd is more reliable than Qbd, Thus we should be careful to use Qbd to evaluate the reliability of 0.18μm or beyond CMOS process gate oxide.

Force and impulse multi-sensor based on flexible gate dielectric field effect transistor

Nowadays,force sensors play an important role in industrial production,electronic information,medical health,and many other fields.Two-dimensional material-based filed effect transistor(2D-FET)sensors are competitive with nano-level size,lower power consumption,and accurate response.However,few of them has the capability of impulse detection which is a path function,expressing the cumulative effect of the force on the particle over a period of time.Herein we fabricated the flexible polymethyl methacrylate(PMMA)gate dielectric MoS_(2)-FET for force and impulse sensor application.We systematically investigated the responses of the sensor to constant force and varying forces,and achieved the conversion factors of the drain current signals(I_(ds))to the detected impulse(I).The applied force was detected and recorded by I_(ds)with a low power consumption of~30 nW.The sensitivity of the device can reach~8000%and the 4×1 sensor array is able to detect and locate the normal force applied on it.Moreover,there was almost no performance loss for the device as left in the air for two months.

Current-collapse suppression and leakage-current decrease in AlGaN/GaN HEMT by sputter-TaN gate-dielectric layer

In this paper, we explore the electrical characteristics of high-electron-mobility transistors(HEMTs) using a TaN/AlGaN/GaN metal insulating semiconductor(MIS) structure. The high-resistance tantalum nitride(TaN) film prepared by magnetron sputtering as the gate dielectric layer of the device achieved an effective reduction of electronic states at the TaN/AlGaN interface, and reducing the gate leakage current of the MIS HEMT, its performance was enhanced. The HEMT exhibited a low gate leakage current of 2.15 × 10^(-7) mA/mm and a breakdown voltage of 1180 V. Furthermore, the MIS HEMT displayed exceptional operational stability during dynamic tests, with dynamic resistance remaining only 1.39 times even under 400 V stress.

Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography

Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

Remote entangling gate between a quantum dot spin and a transmon qubit mediated by microwave photons

Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information processors.Designing a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging goal.Here we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state qubits.We employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon qubit.Since this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual photons.By utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter conditions.Further utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various parameters.These results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.

Study of a Hydraulic Jump in an Asymmetric Trapezoidal Channel with Different Sluice Gates

In this study,the main properties of the hydraulic jump in an asymmetric trapezoidal flume are analyzed experimentally,including the so-called sequent depths,characteristic lengths,and efficiency.In particular,an asymmetric trapezoidal flume with a length of 7 m and a width of 0.304 m is considered,with the bottom of the flume transversely inclined at an angle of m=0.296 and vertical lateral sides.The corresponding inflow Froude number is allowed to range in the interval(1.40<F1<6.11).The properties of this jump are compared to those of hydraulic jumps in channels with other types of cross-sections.A relationship for calculating hydraulic jump efficiency is proposed for the considered flume.For F1>5,the hydraulic jump is found to be more effective than that occurring in triangular and symmetric trapezoidal channels.Also,when■mes>8 and■>5,the hydraulic jump in the asymmetrical trapezoidal channel downstream of a parallelogram sluice gate is completely formed as opposed to the situation where a triangular sluice is considered.

Gated Neural Network-Based Unsteady Aerodynamic Modeling for Large Angles of Attack

Modeling of unsteady aerodynamic loads at high angles of attack using a small amount of experimental or simulation data to construct predictive models for unknown states can greatly improve the efficiency of aircraft unsteady aerodynamic design and flight dynamics analysis.In this paper,aiming at the problems of poor generalization of traditional aerodynamic models and intelligent models,an intelligent aerodynamic modeling method based on gated neural units is proposed.The time memory characteristics of the gated neural unit is fully utilized,thus the nonlinear flow field characterization ability of the learning and training process is enhanced,and the generalization ability of the whole prediction model is improved.The prediction and verification of the model are carried out under the maneuvering flight condition of NACA0015 airfoil.The results show that the model has good adaptability.In the interpolation prediction,the maximum prediction error of the lift and drag coefficients and the moment coefficient does not exceed 10%,which can basically represent the variation characteristics of the entire flow field.In the construction of extrapolation models,the training model based on the strong nonlinear data has good accuracy for weak nonlinear prediction.Furthermore,the error is larger,even exceeding 20%,which indicates that the extrapolation and generalization capabilities need to be further optimized by integrating physical models.Compared with the conventional state space equation model,the proposed method can improve the extrapolation accuracy and efficiency by 78%and 60%,respectively,which demonstrates the applied potential of this method in aerodynamic modeling.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy source.Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants.This study proposes an integrated deep learning-based photovoltaic resource assessment method.Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time.The proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment.The proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and landscape.The experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.

Learning algorithm and application of quantum BP neural networks based on universal quantum gates

A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Hydraulic calculation and dynamic analysis of columnar reversing gate

According to the hydraulic calculation principles of the orifice outflow, the discharge capacity of the columnar reversing gate under the partial opening condition was calculated and checked. Using ANSYS, a large finite element analysis software, the discharge process was simulated. The distribution rule of the velocities in the gate chamber and downstream channel was obtained. An FEM model of the columnar reversing gate was built, and the natural vibration properties of the gate were analyzed. Based on the Westergaard added mass method, the added mass caused by the fluid-structure coupling motion was taken into account, and the effects of the coupling interaction were discussed. The results show that the size of the small gates meets the demand for discharge capacity, the current in the gate chamber is quite turbulent, the trunnion and arms are obviously impacted by flow, and the effects of water on vibration characteristics are remarkable. The study provides a reference for the design and calculation of gates of the same type.

Challenges in Atomic-Scale Characterization of High-k Dielectrics and Metal Gate Electrodes for Advanced CMOS Gate Stacks

The decreasing feature sizes in complementary metal-oxide semiconductor （CMOS） transistor technology will require the replacement of SiO2 with gate dielectrics that have a high dielectric constant （high-k） because as the SiO2 gate thickness is reduced below 1.4 nm, electron tunnelling effects and high leakage currents occur in SiO2, which present serious obstacles to future device reliability. In recent years significant progress has been made on the screening and selection of high-k gate dielectrics, understanding their physical properties, and their integration into CMOS technology. Now the family of hafnium oxide-based materials has emerged as the leading candidate for high-k gate dielectrics due to their excellent physical properties. It is also realized that the high-k oxides must be implemented in conjunction with metal gate electrodes to get sufficient potential for CMOS continue scaling. In the advanced nanoscale Si-based CMOS devices, the composition and thickness of interfacial layers in the gate stacks determine the critical performance of devices. Therefore, detailed atomic- scale understandings of the microstructures and interfacial structures built in the advanced CMOS gate stacks, are highly required. In this paper, several high-resolution electron, ion, and photon-based techniques currently used to characterize the high-k gate dielectrics and interfaces at atomic-scale, are reviewed. Particularly, we critically review the research progress on the characterization of interface behavior and structural evolution in the high-k gate dielectrics by high-resolution transmission electron microscopy （HRTEM） and the related techniques based on scanning transmission electron microscopy （STEM）, including high-angle annular dark- field （HAADF） imaging （also known as Z-contrast imaging）, electron energy-loss spectroscopy （EELS）, and energy dispersive X-ray spectroscopy （EDS）, due to that HRTEM and STEM have become essential metrology tools for characterizing the dielectric gate stacks in the present and future generations of CMOS devices. In Section 1 of this review, the working principles of each technique are briefly introduced and their key features are outlined. In Section 2, microstructural characterizations of high-k gate dielectrics at atomic-scale by electron microscopy are critically reviewed by citing some recent results reported on high-k gate dielectrics. In Section 3, metal gate electrodes and the interfacial structures between high-k dielectrics and metal gates are discussed. The electron beam damage effects in high-k gate stacks are also evaluated, and their origins and prevention are described in Section 4. Finally, we end this review with personal perspectives towards the future challenges of atomic-scale material characterization in advanced CMOS gate stacks.

Generation of four-atom cluster states in thermal cavity and implementing remote controlled not gate

We propose an experimentally feasible scheme for preparing a four-atom cluster state in a thermal cavity. In the scheme, the cavity field is only virtually excited and the photon-number-dependent part in the effective Hamiltonian is cancelled so that the system is insensitive to the cavity decay and the thermal field. At the same time, the scheme can be generalized to prepare n-atom cluster states with the success probability 100%. In addition, using the four-atom cluster state, we also propose a simpler scheme for implementing a remote-controlled not gate （CNOT） without the Bell states measurement.

Performance and analytical modelling of halo-doped surrounding gate MOSFETs

Halo structure is added to sub-100 nm surrounding-gate metal-oxide-semiconductor fieldeffect-transistors （MOS- FETs） to suppress short channel effect. This paper develops the analytical surface potential and threshold voltage models based on the solution of Poisson＇s equation in fully depleted condition for symmetric halo-doped cylindrical surrounding gate MOSFETs. The performance of the halo-doped device is studied and the validity of the analytical models is verified by comparing the analytical results with the simulated data by three dimensional numerical device simulator Davinci. It shows that the halo doping profile exhibits better performance in suppressing threshold voltage roll-off and drain-induced barrier lowering, and increasing carrier transport efficiency. The derived analytical models are in good agreement with Davinci.

THERMAL-CONDUCTIVITY MEASUREMENTS FOR FILLING MATERIALS FOR SLIDING GATES AND THEIR EFFECTS ON FREE- TAPPING PERFORMANCE OF STEEL LADLE

The thermal conductivities of different typical species filling materials for sliding gate systems for steel ladles were measured using a hot wire technique, and the relationships between thermal conductivity and temperature were regressed. The factors affecting thermal conductivity were analyzed by using variance analysis for a perpendicular experiment. The effects of thermal conductivity on sliding gate free tapping were studied, and it was revealed that decreasing the thermal conductivity of the filling materials is beneficial in enhancing the rate of free tapping for sliding gates.

A uniform doping ultra-thin SOI LDMOS with accumulation-mode extended gate and back-side etching technology

A uniform doping ultra-thin silicon-on-insulator（SOI） lateral-double-diffused metal-oxide-semiconductor（LDMOS）with low specific on-resistance（R_on,sp） and high breakdown voltage（BV） is proposed and its mechanism is investigated.The proposed LDMOS features an accumulation-mode extended gate（AG） and back-side etching（BE）. The extended gate consists of a P– region and two diodes in series. In the on-state with VGD〉 0, an electron accumulation layer is formed along the drift region surface under the AG. It provides an ultra-low resistance current path along the whole drift region surface and thus the novel device obtains a low temperature distribution. The R_on,sp is nearly independent of the doping concentration of the drift region. In the off-state, the AG not only modulates the surface electric field distribution and improves the BV, but also brings in a charge compensation effect to further reduce the R_on,sp. Moreover, the BE avoids vertical premature breakdown to obtain high BV and allows a uniform doping in the drift region, which avoids the variable lateral doping（VLD） and the ＂hot-spot＂ caused by the VLD. Compared with the VLD SOI LDMOS, the proposed device simultaneously reduces the R_on,sp by 70.2% and increases the BV from 776 V to 818 V.