Ta-Doped Sb2Te Allows Ultrafast Phase-Change Memory with Excellent High-Temperature Operation Characteristics

Phase-change memory(PCM)has considerable promise for new applications based on von Neumann and emerging neuromorphic computing systems.However,a key challenge in harnessing the advantages of PCM devices is achieving high-speed operation of these devices at elevated temperatures,which is critical for the efficient processing and reliable storage of data at full capacity.Herein,we report a novel PCM device based on Ta-doped antimony telluride(Sb2Te),which exhibits both high-speed characteristics and excellent high-temperature characteristics,with an operation speed of 2 ns,endurance of >106 cycles,and reversible switching at 140℃.The high coordination number of Ta and the strong bonds between Ta and Sb/Te atoms contribute to the robustness of the amorphous structure,which improves the thermal stability.Furthermore,the small grains in the three-dimensional limit lead to an increased energy efficiency and a reduced risk of layer segregation,reducing the power consumption and improving the long-term endurance.Our findings for this new Ta-Sb2Te material system can facilitate the development of PCMs with improved performance and novel applications.

Characteristics of Sn-Doped Ge2Sb2Te5 Films Used for Phase-Change Memory

rfmagnetron 劈啪作响在 Si (100 )/SiO2 底层上扔的做 Sn 的 Ge2Sb2Te5 薄电影被微分扫描热量计， X 光衍射和表抵抗测量调查。3.58 at.% ， 6.92 at.% 和 10.04at .% 的结晶化温度分别地，做 Sn 的 Ge2Sb2Te5 薄电影有 5.3， 6.1 和 0.9 度 C 的减少它是有益的为 amorphous-to-crystalline 阶段转变减少切换的水流。由于做 Sn，水晶的 Ge2Sb2Te5 薄电影的表电阻关于 2-10 时间增加，它可能是有用的为 theamorphous-to-crystalline 阶段变化减少切换的水流。另外，为处于水晶的状态的做 Sn 的 Ge2Sb2Te5 薄电影的表电阻的明显的减少可分散性被观察了，它能在为阶段变化记忆房间元素数组最小化电阻差别起一个重要作用。

Phase-change memory based on matched Ge-Te,Sb-Te,and In-Te octahedrons:Improved electrical performances and robust thermal stability

Phase-change memory(PCM)has been developed for three-dimensional(3D)data storage devices,posing huge challenges to the thermal stability and reliability of PCM.However,the low thermal stability of Ge2Sb2Te5(GST)limits further application.Here,we demonstrate PCM based on In0.9Ge2Sb2Te5(IGST)alloy,showing 180C 10-years data retention,6 ns set speed,one order of magnitude longer life time,and 75%reduced power consumption compared to GST-based device.The In can occupy the cationic positions and the In-Te octahedrons with good phase-change properties can geometrically match well with the host Ge-Te and Sb-Te octahedrons,acting as nucleation centers to boost the set speed and enhance the endurance of IGST device.Introducing stable matched phase-change octahedrons can be a feasible way to achieve practical PCMs.

Tailoring the oxygen concentration in Ge-Sb-O alloys to enable femtojoule-level phase-change memory operations

Chalcogenide phase-change materials(PCMs),in particular,the flagship Ge2Sb2Te5(GST),are leading candidates for advanced memory applications.Yet,GST in conventional devices suffer from high power consumption,because the RESET operation requires melting of the crystalline GST phase.Recently,we have developed a conductive-bridge scheme for low-power phase-change application utilizing a self-decomposed Ge-Sb-O(GSO)alloy.In this work,we present thorough structural and electrical characterizations of GSO thin films by tailoring the concentration of oxygen in the phase-separating GSO system.We elucidate a two-step process in the as-deposited amorphous film upon the introduction of oxygen:with increasing oxygen doping level,germanium oxides form first,followed by antimony oxides.To enable the conductive-bridge switching mode for femtojoule-level RESET energy,the oxygen content should be sufficiently low to keep the antimony-rich domains easily crystallized under external electrical stimulus.Our work serves as a useful example to exploit alloy decomposition that develops heterogeneous PCMs,minimizing the active switching volume for low-power electronics.

Universal memory based on phase-change materials:From phase-change random access memory to optoelectronic hybrid storage

The era of information explosion is coming and information need to be continuously stored and randomly accessed over long-term periods,which constitute an insurmountable challenge for existing data centers.At present,computing devices use the von Neumann architecture with separate computing and memory units,which exposes the shortcomings of“memory bottleneck”.Nonvolatile memristor can realize data storage and in-memory computing at the same time and promises to overcome this bottleneck.Phase-change random access memory(PCRAM)is called one of the best solutions for next generation non-volatile memory.Due to its high speed,good data retention,high density,low power consumption,PCRAM has the broad commercial prospects in the in-memory computing application.In this review,the research progress of phase-change materials and device structures for PCRAM,as well as the most critical performances for a universal memory,such as speed,capacity,and power consumption,are reviewed.By comparing the advantages and disadvantages of phase-change optical disk and PCRAM,a new concept of optoelectronic hybrid storage based on phase-change material is proposed.Furthermore,its feasibility to replace existing memory technologies as a universal memory is also discussed as well.

Unconventional phase transition of phase-change-memory materials for optical data storage

Recent years, optically controlled phase-change memory draws intensive attention owing to some advanced applications including integrated all-optical nonvolatile memory, in-memory computing, and neuromorphic computing. The light-induced phase transition is the key for this technology. Traditional understanding on the role of light is the heating effect. Generally, the RESET operation of phase-change memory is believed to be a melt-quenching-amorphization process. However, some recent experimental and theoretical investigations have revealed that ultrafast laser can manipulate the structures of phase-change materials by non-thermal effects and induces unconventional phase transitions including solid-to-solid amorphization and order-to-order phase transitions. Compared with the conventional thermal amorphization,these transitions have potential superiors such as faster speed, better endurance, and low power consumption. This article summarizes some recent progress of experimental observations and theoretical analyses on these unconventional phase transitions. The discussions mainly focus on the physical mechanism at atomic scale to provide guidance to control the phase transitions for optical storage. Outlook on some possible applications of the non-thermal phase transition is also presented to develop new types of devices.

Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism

Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

The Impact of Opioid Drugs on Memory and Other Cognitive Functions: A Review

Background and Purpose: Opioids, used for centuries to alleviate pain, have become a double-edged sword. While effective, they come with a host of adverse effects, including memory and cognition impairment. This review delves into the impact of opioid drugs on cognitive functions, explores underlying mechanisms, and investigates their prevalence in both medical care and illicit drug use. The ultimate goal is to find ways to mitigate their potential harm and address the ongoing opioid crisis. Methods: We sourced data from PubMed and Google Scholar, employing search combinations like “opioids,” “memory,” “cognition,” “amnesia,” “cognitive function,” “executive function,” and “inhibition.” Our focus was on English-language articles spanning from the inception of these databases up to the present. Results: The literature consistently reveals that opioid use, particularly at high doses, adversely affects memory and other cognitive functions. Longer deliberation times, impaired decision-making, impulsivity, and behavioral disorders are common consequences. Chronic high-dose opioid use is associated with conditions such as amnesiac syndrome (OAS), post-operative cognitive dysfunction (POCD), neonatal abstinence syndrome (NAS), depression, anxiety, sedation, and addiction. Alarming trends show increased opioid use over recent decades, amplifying the risk of these outcomes. Conclusion: Opioids cast a shadow over memory and cognitive function. These effects range from amnesiac effects, lessened cognitive function, depression, and more. Contributing factors include over-prescription, misuse, misinformation, and prohibition policies. Focusing on correct informational campaigns, removing punitive policies, and focusing on harm reduction strategies have been shown to lessen the abuse and use of opioids and thus helping to mitigate the adverse effects of these drugs. Further research into the impacts of opioids on cognitive abilities is also needed as they are well demonstrated in the literature, but the mechanism is not often completely understood.

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

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.

Garcinia Kola and Kolaviron Attenuates Bisphenol A-Induced Memory Impairment and Hippocampal Neuroinflammation in Male Wistar Rats

Bisphenol A (BPA), a toxicant which can leach into food from plastic containers, is reported to induce neurotoxicity among others via oxidative mechanisms. However, antioxidant compounds have been suggested to mitigate BPA-induced toxicities. Garcinia kola (GK) and its bioactive compound, kolaviron, are well-established natural antioxidants, which can exert protective effects against BPA-induced toxicities. This study was designed to investigate the likely mitigating effect of GK and kolaviron on BPA-induced memory impairment and hippocampal neuroinflammation in male Wistar rats. Thirty-five rats were equally grouped and treated as follows: I and II received distilled water and corn oil, respectively at 0.2 mL, while III - VII received BPA (50 mg/kg), BPA + GK (200 mg/kg), BPA + kolaviron (200 mg/kg), GK and kolaviron, respectively for 28 days p.o. Thereafter, behavioral studies were done using the Novel Object Recognition and Y maze tests. Subsequently under anaesthesia, the hippocampus in each animal was dissected out, homogenized and analysed for malondialdehyde, superoxide dismutase, catalase, reduced glutathione, glutathione transferase, nitrites, interleukin-6, tumour necrosis factor-α, acetylcholinesterase, glutamate acid decarboxylase, and arginase activity. Data were analyzed by ANOVA and Tukey Post-hoc test at p p Garcinia kola and Kolaviron mitigate bisphenol A-induced memory impairment and neuroinflammation via antioxidant potentiation and neurotransmitter balance.

Memory effect in time fractional Schrödinger equation

A significant obstacle impeding the advancement of the time fractional Schrodinger equation lies in the challenge of determining its precise mathematical formulation.In order to address this,we undertake an exploration of the time fractional Schrodinger equation within the context of a non-Markovian environment.By leveraging a two-level atom as an illustrative case,we find that the choice to raise i to the order of the time derivative is inappropriate.In contrast to the conventional approach used to depict the dynamic evolution of quantum states in a non-Markovian environment,the time fractional Schrodinger equation,when devoid of fractional-order operations on the imaginary unit i,emerges as a more intuitively comprehensible framework in physics and offers greater simplicity in computational aspects.Meanwhile,we also prove that it is meaningless to study the memory of time fractional Schrodinger equation with time derivative 1<α≤2.It should be noted that we have not yet constructed an open system that can be fully described by the time fractional Schrodinger equation.This will be the focus of future research.Our study might provide a new perspective on the role of time fractional Schrodinger equation.

Improvement of Electrical Properties of the Ge2Sb2Te5 Film by Doping Si for Phase-Change Random Access Memory

做 Si 的 Ge (2 ) sb (2 ) Te (5 ) 电影被 dc 劈啪作响 magnetronco 与 Ge2Sb2Te5 和 Si 目标准备了。在在两结晶化温度和阶段转变温度 fromface-centred-cubic (fcc ) 的增加的 Te (5 ) 电影结果分阶段执行到的 Ge (2 ) sb (2 ) 的 Si 的增加六角形(十六进制) 阶段。Ge2Sb2Te5 电影的抵抗力显示出重要增加， Si 做。当在这部电影做 Si 的 11.8 at.% 时，在退火的 460 度 C 以后的抵抗力与 undoped Ge2Sb2Te5 电影相比从 64 ～ 99 终止从 1 ～ 11 m 终止(.) 厘米和动态抵抗增加增加。这对写阶段变化随机存取记忆的当前的减小很有用。

A novel one-time-programmable memory unit based on Schottky-type p-GaN diode

In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state through Schottky junction breakdown,and the state is permanently preserved.The memory unit features a current ratio of more than 10^(3),a read voltage window of 6 V,a programming time of less than 10^(−4)s,a stability of more than 108 read cycles,and a lifetime of far more than 10 years.Besides,the fabrication of the device is fully compatible with commercial Si-based GaN process platforms,which is of great significance for the realization of low-cost read-only memory in all-GaN integration.

Electromagnetic Performance Analysis of Variable Flux Memory Machines with Series-magnetic-circuit and Different Rotor Topologies

In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Relationship between physical activity and specific working memory indicators of depressive symptoms in university students

BACKGROUND The detection rate of depression among university students has been increasing in recent years,becoming one of the main psychological diseases that endangers their physical and mental health.According to statistics,self-harm and suicide,for which there is no effective intervention,are the second leading causes of death.AIM To explore the relationship between different elements and levels of physical activity and college students’depression-symptom-specific working memory indicators.METHODS Of 143 college students were analyzed using the Beck Depression Self-Rating Scale,the Physical Activity Rating Scale,and the Working Memory Task.RESULTS There was a significant difference between college students with depressive symptoms and healthy college students in completing verbal and spatial working memory(SWM)tasks correctly(all P<0.01).Physical Activity Scale-3 scores were significantly and positively correlated with the correct rate of the verbal working memory task(r=0.166)and the correct rate of the SWM task(r=0.210)(all P<0.05).There were significant differences in the correct rates of verbal and SWM tasks according to different exercise intensities(all P<0.05)and different exercise durations(all P<0.05),and no significant differences in the correct rates of verbal and SWM tasks by exercise frequency(all P>0.05).CONCLUSION An increase in physical exercise among college students,particularly medium-and high-intensity exercise and exercise of 30 min or more,can improve the correct rate of completing working memory tasks.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

Dynamics of Plate Equations with Memory Driven by Multiplicative Noise on Bounded Domains

This article examines the dynamics for stochastic plate equations with linear memory in the case of bounded domain. We investigate the existence of solutions and bounded absorbing set by using the uniform pullback attractors on the tails estimates, and the asymptotic compactness of the random dynamical system is proved by decomposition method, and then we obtain the existence of a random attractor.