The application of halide perovskites in memristors

New neuromorphic architectures and memory technologies with low power consumption,scalability and high-speed are in the spotlight due to the von Neumann bottleneck and limitations of Moore’s law.The memristor,a two-terminal synaptic device,shows powerful capabilities in neuromorphic computing and information storage applications.Active materials with high defect migration speed and low defect migration barrier are highly promising for high-performance memristors.Halide perovskite(HP)materials with point defects(such as gaps,vacancies,and inversions)have strong application potential in memristors.In this article,we review recent advances on HP memristors with exceptional performances.First,the working mechanisms of memristors are described.Then,the structures and properties of HPs are explained.Both electrical and photonic HP-based memristors are overviewed and discussed.Different fabrication methods of HP memristor devices and arrays are described and compared.Finally,the challenges in integrating HP memristors with complementary metal oxide semiconductors(CMOS)are briefly discussed.This review can assist in developing HP memristors for the next-generation information technology.

Charge trapping memory device based on the Ga2O3 films as trapping and blocking layer

We present a new charge trapping memory(CTM) device with the Au/Ga2O3/SiO2/Si structure, which is fabricated by using the magnetron sputtering, high-temperature annealing, and vacuum evaporation techniques. Transmission electron microscopy diagrams show that the thickness of the SiO2 tunneling layer can be controlled by the annealing temperature.When the devices are annealed at 760℃, the measured C-V hysteresis curves exhibit a maximum 6 V memory window under a ±13 V sweeping voltage. In addition, a slight degradation of the device voltage and capacitance indicates the robust retention properties of flat-band voltage and high/low state capacitance. These distinctive advantages are attributed to oxygen vacancies and inter-diffusion layers, which play a critical role in the charge trapping process.

A high-speed true random number generator based on Ag/SiNx/n-Si memristor

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness,this variability is valuable for safe applications in hardware,such as the true random number generator(TRNG).However,the speed of TRNG is still be further improved.Here,we propose a reliable Ag/SiNx/n-Si volatile memristor,which exhibits a typical threshold switching device with stable repeat ability and fast switching speed.This volatile-memristor-based TRNG is combined with nonlinear feedback shift register(NFSR)to form a new type of high-speed dual output TRNG.Interestingly,the bit generation rate reaches a high speed of 112 kb/s.In addition,this new TRNG passed all 15 National Institute of Standards and Technology(NIST)randomness tests without post-processing steps,proving its performance as a hardware security application.This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security.

A review of Mott insulator in memristors:The materials,characteristics,applications for future computing systems and neuromorphic computing

Mott insulator material,as a kind of strongly correlated electronic system with the characteristic of a drastic change in electrical conductivity,shows excellent application prospects in neuromorphological calculations and has attracted significant attention in the scientific community.Especially,computing systems based on Mott insulators can overcome the bottleneck of separated data storage and calculation in traditional artificial intelligence systems based on the von Neumann architecture,with the potential to save energy,increase operation speed,improve integration,scalability,and three-dimensionally stacked,and more suitable to neuromorphic computing than a complementary metal-oxide-semiconductor.In this review,we have reviewed Mott insulator materials,methods for driving Mott insulator transformation(pressure-,voltage-,and temperature-driven approaches),and recent relevant applications in neuromorphic calculations.The results in this review provide a path for further study of the applications in neuromorphic calculations based on Mott insulator materials and the related devices.

Reconfigurable memristor based on SrTiO_(3) thin-film for neuromorphic computing

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain.However,the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail.In this work,Ag/SrTiO_(3)(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip.By modulating the compliance current,two types of resistance switching,volatile and nonvolatile,can be obtained in amorphous STO thin film.This is attributed to the manipulation of the Ag conductive filament.Furthermore,through regulating electrical pulses and designing bionic circuits,the neuronal functions of leaky integrate and fire,as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation,are successfully realized.This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems.

利用界面工程来调控铁电隧道忆阻器的生物突触行为

界面工程一直是调节铁电隧道结忆阻器(FTM)行为的重要途径,且直接影响其生物突触特性.为了研究界面对人工突触性能的影响,本工作中,我们研究了具有Pt/BaTiO_(3)/La_(0.67)Sr_(0.33)MnO_(3)结构的忆阻器.其中可以通过控制SrTiO_(3)(STO)衬底的终止层和BaTiO_(3)(BTO)薄膜层状生长模式来控制忆阻器器件的界面.由于BTO薄膜相反的铁电极化方向以及与之对应的不同的能带结构,具有不同界面的FTM呈现出相反的电阻开关行为.更重要的是,FTM的突触学习特性也可以通过控制界面来调整.具有不同接口终端的FTM可以调节长时程增强、长时程抑制、尖峰时间依赖性可塑性和配对脉冲促进的不同特性.基于这两种接口工程FTM的突触行为,可以构建人工神经网络系统来完成手写数字图像识别过程,两者的准确率都接近90%.我们的结果为通过纳米级界面工程调整忆阻器的功能提供了有用的参考.

A multimode-fused sensory memory system based on a robust self-assembly nanoscaffolded BaTiO_(3):Eu_(2)O_(3) memristor

Biologically inspired neuromorphic sensory memory systems based on memristor have received a lot of attention in the booming artificial intelligence industry due to significant potential to effectively process multi-sensory signals from complex external environments.However,many memristors have significant switching parameters disperse,which is a great challenge for using memristors in bionic neuromorphic sensory memory systems.Herein,a stable ferroelectric memristor based on the Pd/BaTiO_(3):Eu2O_(3)/La0.67Sr0.33MnO_(3)grown on Silicon structure with SrTiO_(3)as buffer layer is presented.The device possesses low coercive field voltage(-1.3-2.1 V)and robust endurance characteristic(~10^(10)cycles)through optimizing the growth temperature.More importantly,an ultra-stable artificial multimodal sensory memory system with visual and tactile functions was reported for the first time by combining a pressure sensor,a photosensitive sensor,and a robotic arm.Utilizing the above system,the sensitivity value of the system is expressed by the conductance of the memristor to realize the gradual change of external stimulus,and multi signals inputs at the same time to this system have faithfully achieved sensory adaptation to multimodal sensors.This work paves the way for future development of memristor-based perception systems in efficient multisensory neural robots.

基于二维α-MoO_(3)的多值存储特性及其双重导电机制研究

二维过渡金属氧化物材料的出现为高密度、低功耗的忆阻器研究提供了机会.其中,α-MoO_(3)作为功能层应用于忆阻器是最有希望的候选材料之一.然而,对α-MoO_(3)基忆阻器的导电机制的研究仍然不足.本工作中,我们制作了cross-point结构的α-MoO_(3)忆阻器,通过电极工程优化了其忆阻性能,并详细研究了其电阻转变机制.通过引入具有Ag/Ti叠层结构的混合电极实现了多值非挥发性存储性能.基于电流-电压曲线拟合和温度依赖特性测试结果,结合高分辨透射电镜微观表征,我们提出了α-MoO_(3)忆阻器的双重导电机制.在电阻转变过程中,阳离子和阴离子的迁移都对电导调制有贡献,两种由Ag和氧空位组成的导电丝同时存在.该器件展现出稳定的忆阻特性,超过103的耐久性、大于104的开关比ROFF/RON、多值存储特性和快速响应(10μs).本工作为二维α-MoO_(3)纳米片在高密度存储中的应用提供了理论基础.

HfAlO-based ferroelectric memristors for artificial synaptic plasticity

Memristors have received much attention for their ability to achieve multi-level storage and synaptic learning.However,the main factor that hinders the application of memristors to simulate neural synapses is the instability of the formation and breakage of conductive filaments inside traditional memristors,which makes it difficult to simulate the function of biological synapses in practice.However,the resistance change of ferroelectric memristors relies on the polarization inversion of the ferroelectric thin film,thus avoiding the above problem.In this study,a Pd/HfAlO/LSMO/STO/Si ferroelectric memristor is proposed,which can achieve resistive switching properties through the combined action of ferroelectricity and oxygen vacancies.The I−V curves show that the device has good stability and uniformity.In addition,the effect of pulse sequence modulation on the conductance was investigated,and the biological synaptic function and learning behavior were simulated successfully.The results of the above studies provide a basis for the development of ferroelectric memristors with neurosynaptic-like behaviors.

Organic Passivation of Deep Defects in Cu(In,Ga)Se_(2) Film for Geometry-Simplified Compound Solar Cells

Diverse defects in copper indium gallium diselenide solar cells cause nonradiative recombination losses and impair device performance.Here,an organic passivation scheme for surface and grain boundary defects is reported,which employs an organic passivation agent to infiltrate the copper indium gallium diselenide thin films.A transparent conductive passivating(TCP)film is then developed by incorporating metal nanowires into the organic polymer and used in solar cells.The TCP films have a transmittance of more than 90%in the visible and nearinfrared spectra and a sheet resistance of~10.5Ω/sq.This leads to improvements in the open-circuit voltage and the efficiency of the organic passivated solar cells compared with control cells and paves the way for novel approaches to copper indium gallium diselenide defect passivation and possibly other compound solar cells.

Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing

As the emerging member of zero-dimension transition metal dichalcogenide,WSe2 quantum dots(QDs)have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size.However,low power consumption and high reliability are still challenges for WSe_(2) QDs-based memristors as synaptic devices.

Flexible memristors as electronic synapses for neuro- inspired computation based on scotch tape-exfoliated mica substrates

Flexible memristor devices based on plastic substrates have attracted considerable attention due to their applications in wearable computers and integrated circuits. However, most plastic-substrate memristors cannot function or be grown in high-temperature environments. In this study, scotch-tape-exfoliated mica was used as the flexible memristor substrate in order to resolve these high-temperature issues. Our TiN/ZHO/IGZO memristor, which was constructed using a thin （10 μm） mica substrate, has superior flexibility and thermostability. After bending it 103 times, the device continues to exhibit exceptional electrical characteristics. It can also be implemented for transitions between high and low resistance states, even in temperatures of up to 300 ℃. More importantly, the biological synaptic characteristics of paired-pulse facilitation/depression （PPF/PPD） and spike- timing-dependent plasticity （STDP） were observed through applying different pulse measurement modes. This work demonstrates that flexible memristor devices on mica substrates may potentially allow for the realization of high-temperature memristor applications for biologically-inspired computing systems.

Memristor based on two-dimensional titania nanosheets for multilevel storage and information processing

A huge amount of data requires the non-volatile memory(NVM)technology to exhibit large-capacity storage and fast calculation speed.To further solve the bottleneck of storage capacity and speed,nano-memristors based on two-dimensional(2D)layered materials are expected to realize NVM.This study proposes the fabrication of an Ag/2D-TiOx/Pt high-performance memristor device based on the 2D titania nanosheet material.The device demonstrates stable electrical characteristics under the direct current(DC)mode,including bipolar resistive switching(RS)behavior,multi-level memristive modes,and retention property.Also,it exhibits low switching voltage(0.42 V/–0.2 V),high R_(OFF)/R_(ON)resistance ratio(105),low switching power(10–9 W/10−5 W),and fast response speed.More importantly,the device realizes information encoding and decoding through a multi-level storage performed by different compliance currents.Multiple devices are connected to the actual circuit to realize a storage function with information processing and programmable characteristics.This work provides a powerful platform for the 2D titania nanosheet application in NVM and information processing.

Flexible artificial synapse based on single-crystalline BiFeO_(3) thin film

Realization of functional flexible artificial synapse is a significant step toward neuromorphic computing.Herein,a flexible artificial synapse based on ferroelectric tunnel junctions(FTJs)is demonstrated,using BiFeO_(3)(BFO)thin film as the functional layer.The inorganic single crystalline FTJs grown on rigid perovskite substrates at high temperatures are integrated with the flexible plastic substrates,by using the water-soluble Sr_(3)Al_(2)O_(6)(SAO)as the sacrificial layer and the following transfer.The transferred freestanding BFO thin film exhibits excellent ferroelectric properties.Moreover,the memristive properties and the brain-like synaptic learning performance of the flexible FTJs are investigated.The results show that multilevel resistance states were maintained well of the flexible artificial synapse,together with their stable synaptic learning properties.Our work indicates the promising opportunity of ferroelectric thin film based flexible synapse used in the future neuromorphic computing system.

Silicon-based epitaxial ferroelectric memristor for high temperature operation in self-assembled vertically aligned BaTiO_(3)-CeO_(2) films

Ferroelectric memristors,as one of the most potential non-volatile memory to meet the rapid development of the artificial intelligence era,have the comprehensive function of simulating brain storage and calculation.However,due to the high dielectric loss of traditional ferroelectric materials,the durability of ferroelectric memristors and Si based integration have a great challenge.Here,we report a silicon-based epitaxial ferroelectric memristor based on self-assembled vertically aligned nanocomposites BaTiO_(3)(BTO)-CeO_(2) films.The BTO-CeO_(2) memristors exhibit a stable resistance switching behavior at a high temperature of 100℃ due to higher Curie temperatures of BTO-CeO_(2) films with in-plane compressive strain.And the endurance of the device can reach the order of magnitude of 1×106 times.More importantly,the device has excellent functions for simulating artificial synaptic behavior,including excitatory post-synaptic current,paired-pulse facilitation,paired-pulse depression,spike-time-dependent plasticity,and short and long-term plasticity.Digits recognition ability of the memristor devices is evaluated though a single-layer perceptron model,in which recognition accuracy of digital can reach 86.78%after 20 training iterations.These results provide new way for epitaxial composite ferroelectric films as memristor medium with high temperature intolerance and better durability integrated on silicon.

Two-Dimensional Borophene:Properties,Fabrication,and Promising Applications

Monoelemental two-dimensional(2D)materials(Xenes)aroused a tremendous attention in 2D science owing to their unique properties and extensive applications.Borophene,one emerging and typical Xene,has been regarded as a promising agent for energy,sensor,and biomedical applications.However,the production of borophene is still a challenge because bulk boron has rather intricate spatial structures and multiple chemical properties.In this review,we describe its excellent properties including the optical,electronic,metallic,semiconducting,photoacoustic,and photothermal properties.The fabrication methods of borophene are also presented including the bottom-up fabrication and the top-down fabrication.In the end,the challenges of borophene in the latest applications are presented and perspectives are discussed.

The role of oxygen vacancies in the high cycling endurance and quantum conductance in BiVO4-based resistive switching memory

Resistive random access memory(RRAM)has emerged as a new discipline promoting the development of new materials and devices toward a broad range of electronic and energy applications.Here,we realized a memristive device with weak dependence on the top electrodes and demonstrated the quantized conductance(QC)nature in BiVO4 matrix.The electronic properties have been investigated by the measurements of I-V curves,where the resistive switching(RS)phenomenon with stable switching ratio and excellent longterm retention capabilities are identified.Two more inert materials,TiN and Pd,are applied as the top electrodes to exclude the influence of electrodes on the RS states and QC behavior.The X-ray photoelectron spectroscopy results and transport measurements reveal that the conductive filament(CF)is composed by elemental bismuth.The naturally existed oxygen vacancies in BiVO4 matrix plays as the role of catalyst in the formation and dissolution of CF in BiVO4-based RRAM device,which is the primary cause for the observed weak dependence of switching performance in this device on the type of top electrodes.Our results clearly illustrate that BiVO4 could be a new idea platform to realize the high scalability,high cycling endurance,and multilevel storage RRAM devices.

Experimental search for high-performance ferroelectric tunnel junctions guided by machine learning

Ferroelectric tunnel junction(FTJ)has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing.However,the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials,fabrication methods and conditions and so on.Here,machine learning(ML)is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios.A dataset consisting of 152 FTJ samples with nine features and one target attribute(i.e.,ON/OFF ratio)is established for ML modeling.Among various ML models,the gradient boosting classification model achieves the highest prediction accuracy.Combining the feature importance analysis based on this model with the association rule mining,it is extracted that the utilizations of{graphene/graphite(Gra)(top),LaNiO_(3)(LNO)(bottom)}and{Gra(top),Ca_(0.96)Ce_(0.04)MnO_(3)(CCMO)(bottom)}electrode pairs are likely to result in high ON/OFF ratios in FTJs.Moreover,two previously unexplored FTJs:Gra/BaTiO_(3)(BTO)/LNO and Gra/BTO/CCMO,are predicted to achieve ON/OFF ratios higher than 1000.Guided by the ML predictions,the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated,which unsurprisingly exhibit≥1000 ON/OFF ratios(~8540 and~7890,respectively).This study demonstrates a new paradigm of developing high-performance FTJs by using ML.