Superelastic properties of nanocrystalline NiTi shape memory alloy produced by thermomechanical processing

Effects of thermomechanical treatment of cold rolling followed by annealing on microstructure and superelastic behavior of the Ni50Ti50 shape memory alloy were studied.Several specimens were produced by copper boat vacuum induction melting.The homogenized specimens were hot rolled and annealed at 900°C.Thereafter,annealed specimens were subjected to cold rolling with different thickness reductions up to 70%.Transmission electron microscopy revealed that the severe cold rolling led to the formation of a mixed microstructure consisting of nanocrystalline and amorphous phases in Ni50Ti50 alloy.After annealing at 400°C for 1 h,the amorphous phase formed in the cold-rolled specimens was crystallized and a nanocrystalline structure formed.Results showed that with increasing thickness reduction during cold rolling,the recoverable strain of Ni50Ti50 alloy was increased during superelastic experiments such that the 70%cold rolled-annealed specimen exhibited about 12%of recoverable strain.Moreover,with increasing thickness reduction,the critical stress for stress-induced martensitic transformation was increased.It is noteworthy that in the 70%cold rolled-annealed specimen,the damping capacity was measured to be 28 J/cm3 that is significantly higher than that of commercial NiTi alloys.

Superelastic behavior of nanostructured Ti_(50)Ni_(48)Co_2 shape memory alloy with cold rolling processing

Effects of cold rolling followed by annealing on microstructural evolution and superelastic properties of the Ti50Ni48Co2 shape memory alloy were investigated. Results showed that during cold rolling, the alloy microstructure evolved through six basic stages including stress-induced martensite transformation and plastic deformation of martensite, deformation twinning, accumulation of dislocations along twin and variant boundaries in martensite, nanocrystallization, amorphization and reverse transformation of martensite to austenite. After annealing at 400 ℃ for 1 h, the amorphous phase formed in the cold-rolled specimens was completely crystallized and an entirely nanocrystalline structure was achieved. The value of stress level of the upper plateau in this nanocrystalline alloy was measured as high as 730 MPa which was significantly higher than that of the coarse-grained Ni50Ti50 and Ti50Ni48Co2 alloys. Moreover, the nanocrystalline Ti50Ni48Co2 alloy had a high damping capacity and considerable efficiency for energy storage.

The study of lithographic variation in resistive random access memory

Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,introducing electrical variation among different RRAM devices.In this work,an optical physical verification methodology for the RRAM array is developed,and the effects of different layout parameters on important electrical characteristics are systematically investigated.The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments.The read resistance is more sensitive to the locations in the array(~30%)than SET/RESET voltage(<10%).The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%,whereas it reduces RRAM read resistance by 4×,resulting in a higher power and area consumption.As such,we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.

Constitutive equation and processing map of equiatomic NiTi shape memory alloy under hot plastic deformation

In order to describe the deformation behavior and the hot workability of equiatomic NiTi shape memory alloy (SMA) during hot deformation, Arrhenius-type constitutive equation and hot processing map of the alloy were developed by hot compression tests at temperatures ranging from 500 to 1100 °C and strain rates ranging from 0.0005 to 0.5 s?1. The results show that the instability region of the hot processing map increases with the increase of deformation extent. The instability occurs in the low and high temperature regions. The instability region presents the adiabatic shear bands at low temperatures, but it exhibits the abnormal growth of the grains at high temperatures. Consequently, it is necessary to avoid processing the equiatomic NiTi SMA in these regions. It is preferable to process the NiTi SMA at the temperatures ranging from 750 to 900 °C.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

A Low-Power,Single-Poly,Non-Volatile Memory for Passive RFID Tags

Single-poly,576bit non-volatile memory is designed and implemented in an SMIC 0.18μm standard CMOS process for the purpose of reducing the cost and power of passive RFID tag chips. The memory bit cell is designed with conventional single-poly pMOS transistors, based on the bi-directional Fowler-Nordheim tunneling effect, and the typical program/erase time is 10ms for every 16bits. A new ,single-ended sense amplifier is proposed to reduce the power dissipation in the current sensing scheme. The average current consumption of the whole memory chip is 0.8μA for the power supply voltage of 1.2V at a reading rate of 640kHz.

A DSP-based Neural Network Signal Processing System

Implementation of artificial neural network(ANN)is very important to theoretical studyand applications of ANN.On the basis of studying existing methods,this paper concentrateson the DSP-based virtual implementation of ANN.A parallel processing system composed ofTMS320C30 has been designed and configured,which ean provide a peak speed as high as100 MFLOPS and a parallel efficiency of 90%(during the forward phase of BP),and can heused for sonar signal processing.Scalability of the system is also studied.

Improvement Design for Distributed Real-Time Stream Processing Systems

In the era of Big Data, typical architecture of distributed real-time stream processing systems is the combination of Flume, Kafka, and Storm. As a kind of distributed message system, Kafka has the characteristics of horizontal scalability and high throughput, which is manly deployed in many areas in order to address the problem of speed mismatch between message producers and consumers. When using Kafka, we need to quickly receive data sent by producers. In addition, we need to send data to consumers quickly. Therefore, the performance of Kafka is of critical importance to the performance of the whole stream processing system. In this paper, we propose the improved design of real-time stream processing systems, and focus on improving the Kafka's data loading process.We use Kafka cat to transfer data from the source to Kafka topic directly, which can reduce the network transmission. We also utilize the memory file system to accelerate the process of data loading, which can address the bottleneck and performance problems caused by disk I/O. Extensive experiments are conducted to evaluate the performance, which show the superiority of our improved design.

Resistive Switching Memory of TiO2 Nanowire Networks Grown on Ti Foil by a Single Hydrothermal Method

The resistive switching characteristics of TiO_2 nanowire networks directly grown on Ti foil by a single-step hydrothermal technique are discussed in this paper. The Ti foil serves as the supply of Ti atoms for growth of the TiO_2 nanowires, making the preparation straightforward. It also acts as a bottom electrode for the device. A top Al electrode was fabricated by e-beam evaporation process. The Al/TiO_2 nanowire networks/Ti device fabricated in this way displayed a highly repeatable and electroforming-free bipolar resistive behavior with retention for more than 10~4 s and an OFF/ON ratio of approximately 70. The switching mechanism of this Al/TiO_2 nanowire networks/Ti device is suggested to arise from the migration of oxygen vacancies under applied electric field. This provides a facile way to obtain metal oxide nanowire-based Re RAM device in the future.

Memory consolidation during sleep and adult hippocampal neurogenesis

In anticipation of the massive burden of neurodegenerative disease within super-aged societies, great efforts have been made to utilize neural stem and progenitor cells for regenerative medicine. The capacity of intrinsic neural stem and progenitor cells to regenerate damaged brain tissue remains unclear, due in part to the lack of knowledge about how these newly born neurons integrate into functional circuitry. As sizable integration of adult-born neurons naturally occurs in the dentate gyrus region of the hippocampus, clarifying the mechanisms of this process could provide insights for applying neural stem and progenitor cells in clinical settings. There is convincing evidence of functional correlations between adult-born neurons and memory consolidation and sleep; therefore, we describe some new advances that were left untouched in our recent review.

A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND SIGNAL PROCESSING

Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification.Signal-processing methods can extract the potent time-frequency-domain characteristics of signals;however,the performance of conventional characteristics-based classification needs to be improved.Widely used deep learning algorithms(e.g.,convolutional neural networks(CNNs))can conduct classification by extracting high-dimensional data features,with outstanding performance.Hence,combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance.A novel vibration recognition method based on signal processing and deep neural networks is proposed herein.First,environmental vibration signals are collected;then,signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms:the wavelet transform,Hilbert-Huang transform,and Mel frequency cepstral coefficient extraction method.Subsequently,CNNs,long short-term memory(LSTM)networks,and combined deep CNN-LSTM networks are trained for vibration recognition,according to the time-frequencydomain characteristics.Finally,the performance of the trained deep neural networks is evaluated and validated.The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.

Radiation Tolerant Viterbi Decoders for On-Board Processing(OBP) in Satellite Communications

Modern satellite communication systems require on-board processing(OBP)for performance improvements,and SRAM-FPGAs are an attractive option for OBP implementation.However,SRAM-FPGAs are sensitive to radiation effects,among which single event upsets(SEUs)are important as they can lead to data corruption and system failure.This paper studies the fault tolerance capability of a SRAM-FPGA implemented Viterbi decoder to SEUs on the user memory.Analysis and fault injection experiments are conducted to verify that over 97%of the SEUs on user memory would not lead to output errors.To achieve a better reliability,selective protection schemes are then proposed to further improve the reliability of the decoder to SEUs on user memory with very small overhead.Although the results are obtained for a specific FPGA implementation,the developed reliability estimation model and the general conclusions still hold for other implementations.

Short-term Memory and Listening Comprehension

More and more attention is paid to listening comprehension and learner factors nowadays, but less attention is paid to the effect of short-term memory. By analyzing the function and effect of short-term memory in the process of information in the mind, this essay points out that short-term memory plays a vital role in listening comprehension, and puts forward three most effective ways to improve short-term memory retention.

Improving the data retention of phase change memory by using a doping element in selected Ge_2Sb_2Te_5

The crystallization characteristics of a ubiquitous T-shaped phase change memory(PCM) cell, under SET current pulse and very small disturb current pulse, have been investigated by finite element modelling. As analyzed in this paper, the crystallization region under SET current pulse presents first on the corner of the bottom electron contact(BEC) and then promptly forms a filament shunting down the amorphous phase to achieve the low-resistance state, whereas the tiny disturb current pulse accelerates crystallization at the axis of symmetry in the phase change material. According to the different crystallization paths, a new structure of phase change material layer is proposed to improve the data retention for PCM without impeding SET operation.This structure only requires one or two additional process steps to dope nitrogen element in the center region of phase change material layer to increase the crystallization temperature in this confined region. The electrical-thermal characteristics of PCM cells with incremental doped radius have been analyzed and the best performance is presented when the doped radius is equal to the radius of the BEC.

Comparison between cannabidiol and sertra⁃line in modulation of post-traumatic stress dis⁃order-like behaviors and fear memory in mice

OBJECTIVE Post-traumatic stress disorder(PTSD)is characterized by poor adapta⁃tion to a traumatic experience and disturbances in fear memory regulation,and currently lacks effective medication.Cannabidiol(CBD)is the primary component of the Cannabis sativa plant;it does not have any psychoactive effects and has been implicated in modulating fear learning in mammals.The present study investigated the effect of CBD on PTSD-like behaviors in a mouse pre-shock model,the effect of CBD in the modulation of trauma-related fear memory,a crucial process leading to core symptoms of PTSD.METHODS Pre-shock model was applied in which mice were submitted to training with two days of 0.8 mA×12 times of foot-shock,and PTSD-like behaviors was evaluated during 3 and 26 d,including freezing time to the conditioned context,open field test,elevated plus maze test and social interaction test.RESULTS CBD(10 mg·kg^(-1))administration alleviated main PTSD-like symptoms in the mouse pre-shock model by attenuating trauma-related fear memory,decreasing anxiety-like behavior,and increasing social interaction behavior.However,sertraline(15 mg·kg^(-1))was only effective when adminis⁃tered throughout the test period.Furthermore,CBD reduced the formation,retrieval,and recon⁃solidation of trauma-related fear memory,whereas sertraline only reduced fear-memory retrieval.Neither CBD nor sertraline influenced the acquisi⁃tion of trauma-related fear memory.CONCLU⁃SION CBD produced anti-PTSD-like actions in mice,and could disrupt trauma-related fear mem⁃ory by interfering with multiple aspects of fear memory processing in mice.These findings indi⁃cate that CBD may be a promising candidate for treating PTSD.

AN INFORMATIC APPROACH TO A LONG MEMORY STATIONARY PROCESS

Long memory is an important phenomenon that arises sometimes in the analysis of time series or spatial data.Most of the definitions concerning the long memory of a stationary process are based on the second-order properties of the process.The mutual information between the past and future I_(p−f) of a stationary process represents the information stored in the history of the process which can be used to predict the future.We suggest that a stationary process can be referred to as long memory if its I_(p−f) is infinite.For a stationary process with finite block entropy,I_(p−f) is equal to the excess entropy,which is the summation of redundancies that relate the convergence rate of the conditional(differential)entropy to the entropy rate.Since the definitions of the I_(p−f) and the excess entropy of a stationary process require a very weak moment condition on the distribution of the process,it can be applied to processes whose distributions are without a bounded second moment.A significant property of I_(p−f) is that it is invariant under one-to-one transformation;this enables us to know the I_(p−f) of a stationary process from other processes.For a stationary Gaussian process,the long memory in the sense of mutual information is more strict than that in the sense of covariance.We demonstrate that the I_(p−f) of fractional Gaussian noise is infinite if and only if the Hurst parameter is H∈(1/2,1).

A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5 microns)is the chief culprit causing aerosol.To forecast the condition of PM2.5,this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5.Since the meteorological data and air pollutes data are typical time series data,it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network(SSHL-LSTMNN)containing memory capability to implement the prediction.However,the number of neurons in the hidden layer is difficult to decide unless manual testing is operated.In order to decide the best structure of the neural network and improve the accuracy of prediction,this paper employs a self-organizing algorithm,which uses Information Processing Capability(IPC)to adjust the number of the hidden neurons automatically during a learning phase.In a word,to predict PM2.5 concentration accurately,this paper proposes the SSHL-LSTMNN to predict PM2.5 concentration.In the experiment,not only the hourly precise prediction but also the daily longer-term prediction is taken into account.At last,the experimental results reflect that SSHL-LSTMNN performs the best.

Comparison between the Requirements of Flowability and Moldability and the Shape Retention of PIM Compacts during Debinding Process

To successfully employ powder injection molding (PIM) as a manufacturing technique, the function of the component, design of the part, material and process should be optimized for overall processing ability of the PIM process. A comparison between the requirements of flowability and moldability and the compacts shape retention has been made in this work. There is often a contradiction between the requirements of flowability and the compacts shape retention. Many works have been done to attain good molding conditions. However, they fail to take into account the effect of some factors that satisfies good molding conditions on the compacts shape retention during debinding. This paper studies the effect of the powder-binder mixture characteristics and the molding conditions on the flowability and moldability and the shape retention of PIM compacts during debinding process so as to attain the benefits of each.

Crystallization Process of Superlattice-Like Sb/SiO_2 Thin Films for Phase Change Memory Application

After compositing with SiO_2 layers, it is shown that superlattice-like Sb/SiO_2 thin films have higher crystallization temperature（~240°C）, larger crystallization activation energy（6.22 e V）, and better data retention ability（189°C for 10 y）. The crystallization of Sb in superlattice-like Sb/SiO_2 thin films is restrained by the multilayer interfaces. The reversible resistance transition can be achieved by an electric pulse as short as 8 ns for the Sb（3 nm）/SiO_2（7 nm）-based phase change memory cell. A lower operation power consumption of 0.09 m W and a good endurance of 3.0 × 10~6 cycles are achieved. In addition, the superlattice-like Sb（3 nm）/SiO_2（7 nm） thin film shows a low thermal conductivity of 0.13 W/（m·K）.