Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

Neuromorphic circuits based on memristors: endowing robots with a human-like brain

Robots are widely used,providing significant convenience in daily life and production.With the rapid development of artificial intelligence and neuromorphic computing in recent years,the realization of more intelligent robots through a pro-found intersection of neuroscience and robotics has received much attention.Neuromorphic circuits based on memristors used to construct hardware neural networks have proved to be a promising solution of shattering traditional control limita-tions in the field of robot control,showcasing characteristics that enhance robot intelligence,speed,and energy efficiency.Start-ing with introducing the working mechanism of memristors and peripheral circuit design,this review gives a comprehensive analysis on the biomimetic information processing and biomimetic driving operations achieved through the utilization of neuro-morphic circuits in brain-like control.Four hardware neural network approaches,including digital-analog hybrid circuit design,novel device structure design,multi-regulation mechanism,and crossbar array,are summarized,which can well simulate the motor decision-making mechanism,multi-information integration and parallel control of brain at the hardware level.It will be definitely conductive to promote the application of memristor-based neuromorphic circuits in areas such as intelligent robotics,artificial intelligence,and neural computing.Finally,a conclusion and future prospects are discussed.

Organic-inorganic halide perovskites for memristors

Organic-inorganic halides perovskites(OHPs)have drawn the attention of many researchers owing to their astonishing and unique optoelectronic properties.They have been extensively used for photovoltaic applications,achieving higher than 26%power conversion efficiency to date.These materials have potential to be deployed for many other applications beyond photovoltaics like photodetectors,sensors,light-emitting diodes(LEDs),and resistors.To address the looming challenge of Moore’s law and the Von Neumann bottleneck,many new technologies regarding the computation of architectures and storage of information are being extensively researched.Since the discovery of the memristor as a fourth component of the circuit,many materials are explored for memristive applications.Lately,researchers have advanced the exploration of OHPs for memristive applications.These materials possess promising memristive properties and various kinds of halide perovskites have been used for different applications that are not only limited to data storage but expand towards artificial synapses,and neuromorphic computing.Herein we summarize the recent advancements of OHPs for memristive applications,their unique electronic properties,fabrication of materials,and current progress in this field with some future perspectives and outlooks.

High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage(0.38 V)and current(200 nA), an extremely steep slope(< 0.1 m V/dec), and a relatively large off/on ratio(> 10^(3)). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.

Exploration and mitigation of protrusion behavior in Ga-ion doped h-BN memristors

Using hexagonal boron nitride(h-BN)to prepare resistive switching devices is a promising strategy.Various doping methods have aroused great interest in the semiconductor field in recent years,but many researchers have overlooked the various repetitive anomalies that occur during the testing process.In this study,the basic electrical properties and additive protrusion behavior of Ga-ion-doped h-BN memristors at micro–nanoscale during the voltage scanning process are investigated via atomic force microscopy(AFM)and energy dispersive spectroscopy.The additive protrusion behavior is subjected to exploratory research,and it is concluded that it is caused by anodic oxidation.An approach is proposed that involves filling the AFM chamber with nitrogen gas to improve the stability of memristor testing,and this method provides a solution for enhanced testing stability of memristors.

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.

Fabrication and investigation of ferroelectric memristors with various synaptic plasticities

In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emulate the synaptic plasticities of the human brain.Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage,low write/read latency and tunable conductive states.However,among the reported ferroelectric memristors,the mechanisms of resistive switching are still under debate.In addition,there needs to be more research on emulation of the brain synapses using ferroelectric memristors.Herein,Cu/PbZr_(0.52)Ti_(0.48)O_(3)(PZT)/Pt ferroelectric memristors have been fabricated.The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior.The synaptic plasticities,including excitatory post-synaptic current,paired-pulse facilitation,paired-pulse depression and spike time-dependent plasticity,have been mimicked by the PZT devices.Furthermore,the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models.This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.

Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing

The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.

Towards engineering in memristors for emerging memory and neuromorphic computing: A review

Resistive random-access memory(RRAM),also known as memristors,having a very simple device structure with two terminals,fulfill almost all of the fundamental requirements of volatile memory,nonvolatile memory,and neuromorphic characteristics.Its memory and neuromorphic behaviors are currently being explored in relation to a range of materials,such as biological materials,perovskites,2D materials,and transition metal oxides.In this review,we discuss the different electrical behaviors exhibited by RRAM devices based on these materials by briefly explaining their corresponding switching mechanisms.We then discuss emergent memory technologies using memristors,together with its potential neuromorphic applications,by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices,in areas such as ION/IOFF ratio,endurance,spike time-dependent plasticity(STDP),and paired-pulse facilitation(PPF),among others.The emulation of essential biological synaptic functions realized in various switching materials,including inorganic metal oxides and new organic materials,as well as diverse device structures such as single-layer and multilayer hetero-structured devices,and crossbar arrays,is analyzed in detail.Finally,we discuss current challenges and future prospects for the development of inorganic and new materials-based memristors.

The influences of model parameters on the characteristics of memristors

As the fourth passive circuit component, a memristor is a nonlinear resistor that can ＂remember＂ the amount of charge passing through it. The characteristic of ＂remembering＂ the charge and non-volatility makes memristors great potential candidates in many fields. Nowadays, only a few groups have the ability to fabricate memristors, and most researchers study them by theoretic analysis and simulation. In this paper, we first analyse the theoretical base and characteristics of memristors, then use a simulation program with integrated circuit emphasis as our tool to simulate the theoretical model of memristors and change the parameters in the model to see the influence of each parameter on the characteristics. Our work supplies researchers engaged in memristor-based circuits with advice on how to choose the proper parameters.

Memory Analysis for Memristors and Memristive Recurrent Neural Networks

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers.Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear drift.Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

Implementation of synaptic learning rules by TaO_(x) memristors embedded with silver nanoparticles

As an alternative device for neuromorphic computing to conquer von Neumann bottleneck,the memristor serving as an artificial synapse has attracted much attention.The TaO^(x) memristors embedded with silver nanoparticles(Ag NPs)have been fabricated to implement synaptic plasticity and to investigate the effects of Ag NPs.The TaO^(x) memristors with and without Ag NPs are capable of simulating synaptic plasticity(PTP,STDP,and STP to LTP),learning,and memory behaviors.The conduction of the high resistance state(HRS) is driven by Schottky-emission mechanism.The embedment of Ag NPs causes the low resistance state(LRS) conduction governed by a Poole-Frenkel emission mechanism instead of a space-charge-limited conduction(SCLC) in a pure TaO^(x) system,which is ascribed to the Ag NPs enhancing electric field to produce additional traps and to reduce Coulomb potential energy of bound electrons to assist electron transport.Consequently,the enhanced electric fields induced by Ag NPs increase the learning strength and learning speed of the synapses.Additionally,they also improve synaptic sensitivity to stimuli.The linearity of conductance modulation and the reproducibility of conductance are improved as well.

SPICE modeling of memristors with multilevel resistance states

With CMOS technologies approaching the scaling ceiling, novel memory technologies have thrived in recent years, among which the memristor is a rather promising candidate for future resistive memory （RRAM）. Memristor＇s potential to store multiple bits of information as different resistance levels allows its application in multilevel cell （MCL） tech- nology, which can significantly increase the memory capacity. However, most existing memristor models are built for binary or continuous memristance switching. In this paper, we propose the simulation program with integrated circuits emphasis （SPICE） modeling of charge-controlled and flux-controlled memristors with multilevel resistance states based on the memristance versus state map. In our model, the memristance switches abruptly between neighboring resistance states. The proposed model allows users to easily set the number of the resistance levels as parameters, and provides the predictability of resistance switching time if the input current/voltage waveform is given. The functionality of our models has been validated in HSPICE. The models can be used in multilevel RRAM modeling as well as in artificial neural network simulations.

Recent advances in memristors based on two-dimensional ferroelectric materials

In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this challenge, the traditional von Neumann architecture-based computing system is out of its depth owing to the separated memory and data processing unit architecture. One of the most effective ways to solve this challenge is building brain inspired computing system with in-memory computing and parallel processing ability based on neuromorphic devices. Therefore, there is a research trend toward the memristors, that can be applied to build neuromorphic computing systems due to their large switching ratio, high storage density, low power consumption, and high stability. Two-dimensional (2D) ferroelectric materials, as novel types of functional materials, show great potential in the preparations of memristors because of the atomic scale thickness, high carrier mobility, mechanical flexibility, and thermal stability. 2D ferroelectric materials can realize resistive switching (RS) because of the presence of natural dipoles whose direction can be flipped with the change of the applied electric field thus producing different polarizations, therefore, making them powerful candidates for future data storage and computing. In this review article, we introduce the physical mechanisms, characterizations, and synthetic methods of 2D ferroelectric materials, and then summarize the applications of 2D ferroelectric materials in memristors for memory and synaptic devices. At last, we deliberate the advantages and future challenges of 2D ferroelectric materials in the application of memristors devices.

Emerging memristors and applications in reservoir computing

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

Emerging multimodal memristors for biorealistic neuromorphic applications

The integration of sensory information from different modalities,such as touch and vision,is essential for organisms to perform behavioral functions such as decision-making,learning,and memory.Artificial implementation of human multi-sensory perception using electronic supports is of great significance for achieving efficient human–machine interaction.Thanks to their structural and functional similarity with biological synapses,memristors are emerging as promising nanodevices for developing artificial neuromorphic perception.Memristive devices can sense multidimensional signals including light,pressure,and sound.Their in-sensor computing architecture represents an ideal platform for efficient multimodal perception.We review recent progress in multimodal memristive technology and its application to neuromorphic perception of complex stimuli carrying visual,olfactory,auditory,and tactile information.At the device level,the operation model and undergoing mechanism have also been introduced.Finally,we discuss the challenges and prospects associated with this rapidly progressing field of research.

Experimental demonstration of SnO_(2) nanofiber-based memristors and their data-driven modeling for nanoelectronic applications

This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and aluminum(Al)electrodes.Thisdevice yielded a very high ROFF:RON of~104(ION:IOFF of~105)with an excellent activation slope of 10 mV/dec,low set voltage ofVSET~1.14 V and good repeatability.This paper physically explained the conduction mechanism in the layered SnO_(2)nanofiber-basedmemristor.The conductive network was composed of nanofibersthat play a vital role in the memristive action,since more conductive paths could facilitate the hopping of electron carriers.Energyband structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claimsreported in this paper.An machine learning(ML)–assisted,datadriven model of the fabricated memristor was also developedemploying different popular algorithms such as polynomialregression,support vector regression,k nearest neighbors,andartificial neural network(ANN)to model the data of the fabricateddevice.We have proposed two types of ANN models(type I andtype II)algorithms,illustrated with a detailed flowchart,to modelthe fabricated memristor.Benchmarking with standard ML techniques shows that the type II ANN algorithm provides the bestmean absolute percentage error of 0.0175 with a 98%R^(2)score.The proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adoptingthe same fabrication recipe,which gave satisfactory predictions.Lastly,the ANN type II model was applied to design and implementsimple AND&OR logic functionalities adopting the fabricatedmemristors with expected,near-ideal characteristics.

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.

Scalable nanocomposite parylene-based memristors:Multifilamentary resistive switching and neuromorphic applications

Memristors are promising candidates for synapse emulation in brain-inspired neuromorphic computing systems.The main obstacle to their usage in such systems is high variability of memristive characteristics and its severe negative effect on the neural network training.This paper addresses the issue from two points of view on the example of the parylene-based memristors:(i)the methods of the memristor internal stochasticity decrease and(ii)the methods of the memristive neural network architecture simplification.The introduction of an optimal Ag nanoparticle concentration(3 vol.%–6 vol.%)to the memristive structure leads to a statistically significant decrease in the switching voltage variation and endurance increase.Moreover,it is shown that post-fabrication annealing improves memristive characteristics,e.g.,resistive switching window increases by an order of magnitude and exceeds 106,the switching voltage variation decreases by a factor of 2(down to 7%for the set and 17%for the reset voltage),and thermostability is improved.Additional transmission electron microscopy and impedance spectroscopy analysis allowed establishing a multifilamentary resistive switching mechanism for nanocomposite parylene-based memristors.The simulation of the formal neural network based on these memristors demonstrates high classification accuracy with low variation for an important biomedical task,heart disease prediction,after careful feature selection and network architecture simplification.Future prospects of the controlled incorporation of the nanocomposite parylene-based memristors in neural networks are brightened by their scaling possibility in crossbar geometry.

A novel multiscale simulation framework for low-dimensional memristors

In recent years,the memristor has been widely considered an emerging device,but it has rarely been simulated.An obstacle is the change in the intrinsic atomic level when it works.Using the density functional theory(DFT),this atomic level change in structure cannot be demonstrated.Using molecular dynamics(MD),memristor electronic transport properties cannot be calculated.In this study,we propose a novel multiscale simulation framework merging MD,DFT,and the nonequilibrium Green’s function method,which can reveal not only a memristor’s basic working mechanism but also its transport character.To verify our framework’s availability in guiding innovative memristor design,a new type of memristor,a planar monolayer MoS_(2)-based memristor,is simulated for the first time.The popped S atoms’effect on its carrier transport is revealed,which clarifies the working mechanism of the planar monolayer MoS_(2)-based memory device.We hope that this framework can shed light on the analysis and design of low-dimensional memristors.