Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering

A Human-Computer Interaction Strategy for An FPGA Platform Boosted Integrated "Perception-Memory" System Based on Electronic Tattoos and Memristors

The integrated "perception-memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform-boosted system that enables the sensing, recognition, and memory for human-computer interaction is reported by the combination of ultra-thin Ag/Al/Paster-based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)-based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative "perception-memory" integrated system not only serves the disabled, enhancing human-computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities.

Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La0.1Bi0.9FeO3 Ferroelectric Tunnel Junctions

Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La0.1Bi0.9FeO3/La0.7Sr0.3MnO3 ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.

Ferroelectric materials for neuroinspired computing applications

In recent years, the emergence of numerous applications of artificial intelligence (AI) has sparked a new technological revolution. These applications include facial recognition, autonomous driving, intelligent robotics, and image restoration. However, the data processing and storage procedures in the conventional von Neumann architecture are discrete, which leads to the "memory wall" problem. As a result, such architecture is incompatible with AI requirements for efficient and sustainable processing. Exploring new computing architectures and material bases is therefore imperative. Inspired by neurobiological systems, in-memory and in-sensor computing techniques provide a new means of overcoming the limitations inherent in the von Neumann architecture. The basis of neural morphological computation is a crossbar array of high-density, high-efficiency non-volatile memory devices. Among the numerous candidate memory devices, ferroelectric memory devices with non-volatile polarization states, low power consumption and strong endurance are expected to be ideal candidates for neuromorphic computing. Further research on the complementary metal-oxide-semiconductor (CMOS) compatibility for these devices is underway and has yielded favorable results. Herein, we first introduce the development of ferroelectric materials as well as their mechanisms of polarization reversal and detail the applications of ferroelectric synaptic devices in artificial neural networks. Subsequently, we introduce the latest developments in ferroelectrics-based in-memory and in-sensor computing. Finally, we review recent works on hafnium-based ferroelectric memory devices with CMOS process compatibility and give a perspective for future developments.

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.

Progressive and Stable Synaptic Plasticity with Femtojoule Energy Consumption by the Interface Engineering of a Metal/Ferroelectric/Semiconductor

In the era of "big data," the cognitive system of the human brain is being mimicked through hardware implementation of highly accurate neuromorphic computing by progressive weight update in synaptic electronics. Low-energy synaptic operation requires both low reading current and short operation time to be applicable to large-scale neuromorphic computing systems. In this study, an energy-efficient synaptic device is implemented comprising a Ni/Pb(Zr_0.52 Ti_0.48 )O₃ (PZT)/0.5 wt.% Nb-doped SrTiO₃ (Nb:STO) heterojunction with a low reading current of 10 nA and short operation time of 20-100 ns. Ultralow femtojoule operation below 9 fJ at a synaptic event, which is comparable to the energy required for synaptic events in the human brain (10 fJ), is achieved by adjusting the Schottky barrier between the top electrode and ferroelectric film. Moreover, progressive domain switching in ferroelectric PZT successfully induces both low nonlinearity/asymmetry and good stability of the weight update. The synaptic device developed here can facilitate the development of large-scale neuromorphic arrays for artificial neural networks with low energy consumption and high accuracy.

Ferroelectricity and Piezoelectricity in 2D Van der Waals CuInP2S6 Ferroelectric Tunnel Junctions

CuInP2S6 (CIPS) is a novel two-dimensional (2D) van der Waals (vdW) ferroelectric layered material with a Curie temperature of T_C~315 K, making it promising for great potential applications in electronic and photoelectric devices. Herein, the ferroelectric and electric properties of CIPS at different thicknesses are carefully evaluated by scanning probe microscopy techniques. Some defects in some local regions due to Cu deficiency lead to a CuInP2S6–In_4/3P₂S₆ (CIPS–IPS) paraelectric phase coexisting with the CIPS ferroelectric phase. An electrochemical strain microscopy (ESM) study reveals that the relaxation times corresponding to the Cu ions and the IPS ionospheres are not the same, with a significant difference in their response to DC voltage, related to the rectification effect of the ferroelectric tunnel junction (FTJ). The electric properties of the FTJ indicate Cu⁺ ion migration and propose that the current flow and device performance are dynamically controlled by an interfacial Schottky barrier. The addition of the ferroelectricity of CIPS opens up applications in memories and sensors, actuators, and even spin-orbit devices based on 2D vdW heterostructures.

Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception

Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.

Enhanced Tunneling Magnetoresistance Effect via Ferroelectric Control of Interface Electronic/Magnetic Reconstructions

Magnetic tunnel junctions (MTJs) with tunable tunneling magnetoresistances (TMR) have already been proven to have great potential for spintronics. Especially, when ferroelectric materials are used as insulating barriers, more novel functions of MTJs can be realized due to interface magnetoelectric coupling. Here, we demonstrate a very large ferroelectric modulation of TMR (as high as 570% in low-resistance state) in the ferroelectric/magnetic La_0.5Sr_0.5MnO₃/BaTiO₃ (LSMO/BTO) junctions and find robust interfacial electronic and magnetic reconstructions via ferroelectric polarization switching. Through electrical, magnetic, and optical measurements combined with X-ray absorption and magnetic circular dichroism, we reveal that the interfacial electronic and magnetic (ferromagnetic/antiferromagnetic phase transition) reconstructions originate from strong electromagnetic coupling between BTO and LSMO at the interface and are driven by the modulation of hole/electron doping at the interface of LSMO/BTO through ferroelectric polarization switching. As a result, the ferroelectrically controlled interface barrier height and width and spin filter effect enable a giant electrical modulation of TMR. Our results shed new light on the intrinsic mechanisms governing magnetoelectric coupling and offering a new route to enhance magnetoelectric coupling for spin control in spintronic devices.

Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr0.7Ca0.3MnO3 Memristor

An amorphous Pr_0.7Ca_0.3MnO₃ (PCMO) film was grown on a TiN/SiO₂/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.

Direct Observation of Interface-Dependent Multidomain State in the BaTiO3 Tunnel Barrier of a Multiferroic Tunnel Junction Memristor

Multiferroic tunnel junctions (MFTJs), normally consisting of a four-state resistance, have been studied extensively as a potential candidate for nonvolatile memory devices. More interestingly, the MFTJs whose resistance can be tuned continuously with applied voltage were also reported recently. Since the performance of MFTJs is closely related to their interfacial structures, it is necessary to investigate MFTJs at the atomic scale. In this work, atomic-resolution HAADF, ABF, and EELS of the La_0.7Sr_0.3MnO₃/BaTiO₃/La_0.7Sr_0.3MnO₃ MFTJ memristor have been obtained with aberration-corrected scanning transmission electron microscopy (STEM). These results demonstrate varied degree of interfacial cation intermixing at the bottom BTO/LSMO interface, which has a direct influence on the polarization of the ferroelectric barrier BTO and the electronic structure of Mn near the interfaces. We also took advantage of a simplified model to explain the relation between the interfacial behavior and polarization states, which could be a contributing factor to the transport properties of this MFTJ.

Low-Temperature Tunneling Electroresistance in Ferromagnetic Metal/Ferroelectric/Semiconductor Tunnel Junctions

Ferroelectric tunnel junctions (FTJs) as artificial synaptic devices are promising candidates for the building block of nonvolatile data storage devices. However, a small ON/OFF ratio of FTJs limits their application in low-temperature operations. In this work, the influence of quantum interference effects on tunneling electroresistance in the La_0.7Sr_0.3MnO₃/BaTiO₃/Nb:SrTiO₃ (ferromagnetic metal/ferroelectric/semiconductor) FTJ at low temperatures is investigated. The Current-voltage curves are observed in the tunnel junction from 300 to 10 K with a six-unit-cell thick BaTiO₃ film by the ferroelectric polarization effect. First, the ON/OFF current ratio increases from 300 to 30 K due to the increase of polarization in the ferroelectric barrier, and then, it gradually decreases when the temperature drops below 30 K. An anomalous ON/OFF current ratio of ∼10⁵ is obtained at 30 K. The low-temperature tunneling properties in the FTJ are associated with a low-temperature resistivity minimum in the ferromagnetic metal layer by the electron-electron interaction, which increases the La_0.7Sr_0.3MnO₃/BaTiO₃ interface resistance, leading to a higher resistance state and lower I_OFF for the OFF state. As a result, the ON/OFF current ratio is abruptly enhanced at 30 K. Our results emphasize the crucial role of transport properties of La_0.7Sr_0.3MnO₃ in FTJs and pave the way for the design and application of FTJs at low temperatures.

Spin-Filtering Ferroelectric Tunnel Junctions as Multiferroic Synapses for Neuromorphic Computing

As nanoelectronic synapses, memristive ferroelectric tunnel junctions (FTJs) have triggered great interest due to the potential applications in neuromorphic computing for emulating biological brains. Here, we demonstrate multiferroic FTJ synapses based on the ferroelectric modulation of spin-filtering BaTiO₃/CoFe₂O₄ composite barriers. Continuous conductance change with an ON/OFF current ratio of ∼54 400% and long-term memory with the spike-timing-dependent plasticity (STDP) of synaptic weight for Hebbian learning are achieved by controlling the polarization switching of BaTiO₃. Supervised learning simulations adopting the STDP results as database for weight training are performed on a crossbar neural network and exhibit a high accuracy rate above 97% for recognition. The polarization switching also alters the band alignment of CoFe₂O₄ barrier relative to the electrodes, giving rise to the change of tunneling magnetoresistance ratio by about 10 times and even the reversal of its sign depending upon the resistance states. These results, especially the electrically switchable spin polarization, provide a new approach toward multiferroic neuromorphic devices with energy-efficient electrical manipulations through potential barrier design. In addition, the availability of spinel ferrite barriers epitaxially grown with ferroelectric oxides also expends the playground of FTJ devices for a broad scope of applications.

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.

Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing

Ferroelectric synapses using polarization switching (a purely electronic switching process) to induce analog conductance change have attracted considerable interest. Here, we propose ferroelectric photovoltaic (FePV) synapses that use polarization-controlled photocurrent as the readout and thus have no limitations on the forms and thicknesses of the constituent ferroelectric and electrode materials. This not only makes FePV synapses easy to fabricate but also reduces the depolarization effect and hence enhances the polarization controllability. As a proof-of-concept implementation, a Pt/Pb(Zr_0.2Ti_0.8)O₃/LaNiO₃ FePV synapse is facilely grown on a silicon substrate, which demonstrates continuous photovoltaic response modulation with good controllability (small nonlinearity and write noise) enabled by gradual polarization switching. Using photovoltaic response as synaptic weight, this device exhibits versatile synaptic functions including long-term potentiation/depression and spike-timing-dependent plasticity. A simulated FePV synapse-based neural network achieves high accuracies (>93%) for image recognition. This study paves a new way toward highly controllable and silicon-compatible synapses for neuromorphic computing.

Comparison of diverse resistive switching characteristics and demonstration of transitions among them in Al-incorporated HfO2-based resistive switching memory for neuromorphic applications

Diverse resistive switching behaviors are observed in the Pt/HfAlO_x/TiN memory device depending on the compliance current, the sweep voltage amplitude, and the bias polarity. We extensively compare three types of resistive switching characteristics in a Pt/HfAlO_x/TiN device in terms of endurance, ON/OFF ratio, linear conductance update, and read margin in a cross-point array structure for synaptic device applications. The bipolar resistive switching under positive set and negative reset shows better linear synaptic weight updates due to gradual switching than the bipolar resistive switching at the opposite polarity. The complementary resistive switching shows a higher read margin due to the current suppression at a low voltage regime. In addition, the potentiation and the depression can be adjusted at the same voltage polarity for a hardware neuromorphic system. Finally, we demonstrate the transition between bipolar resistive switching and complementary resistive switching, which could provide flexibility for different applications.

Diverse resistive switching behaviors are observed in the Pt/HfAlO_x/TiN memory device depending on the compliance current, the sweep voltage amplitude, and the bias polarity.

Perpendicular Magnetic Anisotropy and Dzyaloshinskii-Moriya Interaction at an Oxide/Ferromagnetic Metal Interface

We report on the study of both perpendicular magnetic anisotropy (PMA) and Dzyaloshinskii-Moriya interaction (DMI) at an oxide/ferromagnetic metal (FM) interface, i.e., BaTiO_{3} (BTO)/CoFeB. Thanks to the functional properties of the BTO film and the capability to precisely control its growth, we are able to distinguish the dominant role of the oxide termination (TiO_{2} vs BaO) from the moderate effect of ferroelectric polarization in the BTO film, on the PMA and DMI at an oxide/FM interface. We find that the interfacial magnetic anisotropy energy of the BaO-BTO/CoFeB structure is 2 times larger than that of the TiO_{2}-BTO/CoFeB, while the DMI of the TiO_{2}-BTO/CoFeB interface is larger. We explain the observed phenomena by first principles calculations, which ascribe them to the different electronic states around the Fermi level at oxide/ferromagnetic metal interfaces and the different spin-flip process. This study paves the way for further investigation of the PMA and DMI at various oxide/FM structures and thus their applications in the promising field of energy-efficient devices.

Electret-Based Organic Synaptic Transistor for Neuromorphic Computing

Neuromorphic computing inspired by the neural systems in human brain will overcome the issue of independent information processing and storage. An artificial synaptic device as a basic unit of a neuromorphic computing system can perform signal processing with low power consumption, and exploring different types of synaptic transistors is essential to provide suitable artificial synaptic devices for artificial intelligence. Hence, for the first time, an electret-based synaptic transistor (EST) is presented, which successfully shows synaptic behaviors including excitatory/inhibitory postsynaptic current, paired-pulse facilitation/depression, long-term memory, and high-pass filtering. Moreover, a neuromorphic computing simulation based on our EST is performed using the handwritten artificial neural network, which exhibits an excellent recognition accuracy (85.88%) after 120 learning epochs, higher than most reported organic synaptic transistors and close to the ideal accuracy (92.11%). Such a novel synaptic device enriches the diversity of synaptic transistors, laying the foundation for the diversified development of the next generation of neuromorphic computing systems.

Sub-nanosecond memristor based on ferroelectric tunnel junction

Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO₃/Nb:SrTiO₃ ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 10³ A cm⁻². The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO₃ electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems.

Memristor devices based on ferroelectric tunnel junctions are promising, but suffer from quite slow switching times. Here, the authors report on ultrafast switching times at and above room temperature of 600ps in Ag/BaTiO3/Nb:SrTiO3 based ferroelectric tunnel junctions.

Unusual Hole and Electron Midgap States and Orbital Reconstructions Induced Huge Ferroelectric Tunneling Electroresistance in BaTiO3/SrTiO3

Oxide heterostructures have attracted a lot of interest because of their rich exotic phenomena and potential applications. Recently, a greatly enhanced tunneling electroresistance (TER) of ferroelectric tunnel junctions (FTJs) has been realized in such heterostructures. However, our understanding on the electronic structure of resistance response with polarization reversal and the origin of huge TER is still lacking. Here, we report on electronic structures, particularly at the interface and surface, and the control of the spontaneous polarization of BaTiO₃ films by changing the termination of a SrTiO₃ substrate. Interestingly, unusual electron and hole midgap states are concurrently formed and accompanied by orbital reconstructions, which determine the ferroelectric polarization orientation in the BaTiO₃/SrTiO₃. Such unusual midgap states, which yield a strong electronic screening effect, reduce the ferroelectric barrier width and height, and pin the ferroelectric polarization, lead to a dramatic enhancement of the TER effect. The midgap states are also observed in BaTiO₃ films on electron-doped Nb/SrTiO₃ revealing its universality. Our result provides new insight into the origin of the huge TER effect and opens a new route for designing ferroelectric tunnel junction-based devices with huge TER through interface engineering.

Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing

Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.

Nonvolatile Memory and Artificial Synapse Based on the Cu/P(VDF-TrFE)/Ni Organic Memtranstor

We demonstrate a flexible nonvolatile multilevel memory and artificial synaptic devices based on the Cu/P(VDF-TrFE)/Ni memtranstor which exhibits pronounced nonlinear magnetoelectric effects at room temperature. The states of the magnetoelectric voltage coefficient α_E of the memtranstor are used to encode binary information. By applying selective electric-field pulses, the states of α_E can be switched repeatedly among 2ⁿ states (n = 1, 2, 3) in a zero dc bias magnetic field. In addition, the magnetoelectric coefficient is used to act as synaptic weight, and the induced magnetoelectric voltage V_ME is regarded as postsynaptic potentials (excitatory or inhibitory). The artificial synaptic devices based on the Cu/P(VDF-TrFE)/Ni memtranstor display the long-term potentiation (depression) and spiking-time-dependent plasticity behaviors. The advantages of a simple structure, flexibility, multilevel, and self-biasing make the Cu/P(VDF-TrFE)/Ni organic memtranstor a promising candidate for applications in nonvolatile memory as well as artificial synaptic devices with low energy consumption.

Spike-shape dependence of the spike-timing dependent synaptic plasticity in ferroelectric-tunnel-junction synapses

Resistive switching (RS) devices have attracted increasing attention for artificial synapse applications in neural networks because of their nonvolatile and analogue resistance changes. Among the neural networks, a spiking neural network (SNN) based on spike-timing-dependent plasticity (STDP) is highly energy efficient. To implement STDP in resistive switching devices, several types of voltage spikes have been proposed to date, but there have been few reports on the relationship between the STDP characteristics and spike types. Here, we report the STDP characteristics implemented in ferroelectric tunnel junctions (FTJs) by several types of spikes. Based on simulated time evolutions of superimposed spikes and taking the nonlinear current-voltage (I-V) characteristics of FTJs into account, we propose equations for simulating the STDP curve parameters of a magnitude of the conductance change (ΔG_max) and a time window (τ_C) from the spike parameters of a peak amplitude (V_peak) and time durations (t_p and t_d) for three spike types: triangle-triangle, rectangular-triangle, and rectangular-rectangular. The power consumption experiments of the STDP revealed that the power consumption under the inactive-synapse condition (spike timing |Δt| > τ_C) was as large as 50–82% of that under the active-synapse condition (|Δt| < τ_C). This finding indicates that the power consumption under the inactive-synapse condition should be reduced to minimize the total power consumption of an SNN implemented by using FTJs as synapses.

Energy-Efficient Artificial Synapses Based on Oxide Tunnel Junctions

The development of artificial synapses has enabled the establishment of brain-inspired computing systems, which provides a promising approach for overcoming the inherent limitations of current computer systems. The two-terminal memristors that faithfully mimic the function of biological synapses have intensive prospects in the neural network field. Here, we propose a high-performance artificial synapse based on oxide tunnel junctions with oxygen vacancy migration. Both short-term and long-term plasticities are mimicked in one device. The oxygen vacancy migration through oxide ultrathin films is utilized to manipulate long-term plasticity. Essential synaptic functions, such as paired pulse facilitation, post-tetanic potentiation, as well as spike-timing-dependent plasticity, are successfully implemented in one device by finely modifying the shape of the pre- and postsynaptic spikes. Ultralow femtojoule energy consumption comparable to that of the human brain indicates its potential application in efficient neuromorphic computing. Oxide tunnel junctions proposed in this work provide an alternative approach for realizing energy-efficient brain-like chips.

Giant Electroresistance in Ferroionic Tunnel Junctions

Oxide-based resistive switching devices, including ferroelectric tunnel junctions and resistance random access memory, are promising candidates for the next-generation non-volatile memory technology. In this work, we propose a ferroionic tunnel junction to realize a giant electroresistance. It functions as a ferroelectric tunnel junction at low resistance state and as a Schottky junction at high resistance state, due to interface engineering through the field-induced migration of oxygen vacancies. An extremely large electroresistance with ON/OFF ratios of 5.1×107 at room temperature and 2.1×109 at 10 K is achieved, using an ultrathin BaTiO3-δ layer as the ferroelectric barrier and a semiconducting Nb-doped SrTiO3 substrate as the bottom electrode. The results point toward an appealing way for the design of high-performance resistive switching devices based on ultrathin oxide heterostructures by ionic controlled interface engineering.

Self-Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors

With the advent of the era of big data, resistive random access memory (RRAM) has become one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of information. However, the switching voltage of the RRAM MDs shows a very broad distribution due to the random formation of the conductive filaments. Here, self-assembled lead sulfide (PbS) quantum dots (QDs) are used to improve the uniformity of switching parameters of RRAM, which is very simple comparing with other methods. The resistive switching (RS) properties of the MD with the self-assembled PbS QDs exhibit better performance than those of MDs with pure-Ga₂ O₃ and randomly distributed PbS QDs, such as a reduced threshold voltage, uniformly distributed SET and RESET voltages, robust retention, fast response time, and low power consumption. This enhanced performance may be attributed to the ordered arrangement of the PbS QDs in the self-assembled PbS QDs which can efficiently guide the growth direction for the conducting filaments. Moreover, biosynaptic functions and plasticity, are implemented successfully in the MD with the self-assembled PbS QDs. This work offers a new method of improving memristor performance, which can significantly expand existing applications and facilitate the development of artificial neural systems.