Low-Power, Self-Rectifying, and Forming-Free Memristor with an Asymmetric Programing Voltage for a High-Density Crossbar Application

High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.

Tunable stochastic memristors for energy-efficient encryption and computing

Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements - security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu0.3Te0.7/HfO2 ('CuTeHO') ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.

Multiphase Reset Induced Reliable Dual-Mode Resistance Switching of the Ta/HfO2/RuO2 Memristor

Higher functionality should be achieved within the device-level switching characteristics to secure the operational possibility of mixed-signal data processing within a memristive crossbar array. This work investigated electroforming-free Ta/HfO2/RuO2 resistive switching devices for digital- and analog-type applications through various structural and electrical analyses. The multiphase reset behavior, induced by the conducting filament modulation and oxygen vacancy generation (annihilation) in the HfO2 layer by interacting with the Ta (RuO2) electrode, was utilized for the switching mode change. Therefore, a single device can manifest stable binary switching between low and high resistance states for the digital mode and the precise 8-bit conductance modulation (256 resistance values) via an optimized pulse application for the analog mode. An in-depth analysis of the operation in different modes and comparing memristors with different electrode structures validate the proposed mechanism. The Ta/HfO2/RuO2 resistive switching device is feasible for a mixed-signal processable memristive array.

Memristive Explainable Artificial Intelligence Hardware

Artificial intelligence (AI) is often considered a black box because it provides optimal answers without clear insight into its decision-making process. To address this black box problem, explainable artificial intelligence (XAI) has emerged, which provides an explanation and interpretation of its decisions, thereby promoting the trustworthiness of AI systems. Here, a memristive XAI hardware framework is presented. This framework incorporates three distinct types of memristors (Mott memristor, valence change memristor, and charge trap memristor), each responsible for performing three essential functions (perturbation, analog multiplication, and integration) required for the XAI hardware implementation. Three memristor arrays with high robustness are fabricated and the image recognition of 3 × 3 testing patterns and their explanation map generation are experimentally demonstrated. Then, a software-based extended system based on the characteristics of this hardware is built, simulating a large-scale image recognition task. The proposed system can perform the XAI operations with only 4.32% of the energy compared to conventional digital systems, enlightening its strong potential for the XAI accelerator.

3D-integrated multilayered physical reservoir array for learning and forecasting time-series information

A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.

Chalcogenide Ovonic Threshold Switching Selector

Today's explosion of data urgently requires memory technologies capable of storing large volumes of data in shorter time frames, a feat unattainable with Flash or DRAM. Intel Optane, commonly referred to as three-dimensional phase change memory, stands out as one of the most promising candidates. The Optane with cross-point architecture is constructed through layering a storage element and a selector known as the ovonic threshold switch (OTS). The OTS device, which employs chalcogenide film, has thereby gathered increased attention in recent years. In this paper, we begin by providing a brief introduction to the discovery process of the OTS phenomenon. Subsequently, we summarize the key electrical parameters of OTS devices and delve into recent explorations of OTS materials, which are categorized as Se-based, Te-based, and S-based material systems. Furthermore, we discuss various models for the OTS switching mechanism, including field-induced nucleation model, as well as several carrier injection models. Additionally, we review the progress and innovations in OTS mechanism research. Finally, we highlight the successful application of OTS devices in three-dimensional high-density memory and offer insights into their promising performance and extensive prospects in emerging applications, such as self-selecting memory and neuromorphic computing.

2Memristor-1Capacitor Integrated Temporal Kernel for High-Dimensional Data Mapping

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2 /TiN memristor and TiN/ZrO2 /Al2 O3 /ZrO2 /TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Implementation of Convolutional Neural Networks in Memristor Crossbar Arrays with Binary Activation and Weight Quantization

We propose a hardware-friendly architecture of a convolutional neural network using a 32 × 32 memristor crossbar array having an overshoot suppression layer. The gradual switching characteristics in both set and reset operations enable the implementation of a 3-bit multilevel operation in a whole array that can be utilized as 16 kernels. Moreover, a binary activation function mapped to the read voltage and ground is introduced to evaluate the result of training with a boundary of 0.5 and its estimated gradient. Additionally, we adopt a fixed kernel method, where inputs are sequentially applied to a crossbar array with a differential memristor pair scheme, reducing unused cell waste. The binary activation has robust characteristics against device state variations, and a neuron circuit is experimentally demonstrated on a customized breadboard. Thanks to the analogue switching characteristics of the memristor device, the accurate vector-matrix multiplication (VMM) operations can be experimentally demonstrated by combining sequential inputs and the weights obtained through tuning operations in the crossbar array. In addition, the feature images extracted by VMM during the hardware inference operations on 100 test samples are classified, and the classification performance by off-chip training is compared with the software results. Finally, inference results depending on the tolerance are statistically verified through several tuning cycles.

Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.

Self-Rectifying Memristors for Three-Dimensional In-Memory Computing

Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102  Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.

Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis

Graphs adequately represent the enormous interconnections among numerous entities in big data, incurring high computational costs in analyzing them with conventional hardware. Physical graph representation (PGR) is an approach that replicates the graph within a physical system, allowing for efficient analysis. This study introduces a cross-wired crossbar array (cwCBA), uniquely connecting diagonal and non-diagonal components in a CBA by a cross-wiring process. The cross-wired diagonal cells enable cwCBA to achieve precise PGR and dynamic node state control. For this purpose, a cwCBA is fabricated using Pt/Ta2 O5 /HfO2 /TiN (PTHT) memristor with high on/off and self-rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision are highlighted, and the practical efficacy is demonstrated for two applications. First, it executes a dynamic path-finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA shows a more accurate inferred distance and ≈1/3800 lower processing complexity than the conventional method. Second, it analyzes the protein-protein interaction (PPI) networks containing self-interacting proteins, which possess intricate characteristics compared to typical graphs. The PPI prediction results exhibit an average of 30.5% and 21.3% improvement in area under the curve and F1-score, respectively, compared to existing algorithms.

Filament-free memristors for computing

Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.

Threshold Modulative Artificial GABAergic Nociceptor

Gamma-aminobutyric acid (GABA) is a crucial inhibitory neurotransmitter of the central nervous system. It modifies the signal threshold of the nociceptor, allowing it to react to external stimuli in various circumstances. Thus, GABAergic behaviors are critical characteristics of adaptive behavior in life. Here, a threshold-modulative artificial GABAergic nociceptor is reported for the first time at a Pt/Ti/Nb2 O5- x /Al2 O3- y /Pt/Ti (top to bottom) of the double charge trapping structure. The Al2 O3- y layer contains deep defect states that function similarly to the GABA neurotransmitter in modulating the signal threshold. Meanwhile, the Nb2 O5- x layer traps volatile charges and produces nociceptive behaviors. The combined dynamics of the two layers readily offer threshold-modulative GABAergic nociceptive behaviors. Based on these GABAergic behaviors, a method of implementing hot- and cold-sensitive thermoreceptors is demonstrated and shows its potential applications in advanced sensory devices.

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era

Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.

Mechanism and Impact of Bipolar Current Voltage Asymmetry in Computational Phase-Change Memory

Nanoscale resistive memory devices are being explored for neuromorphic and in-memory computing. However, non-ideal device characteristics of read noise and resistance drift pose significant challenges to the achievable computational precision. Here, it is shown that there is an additional non-ideality that can impact computational precision, namely the bias-polarity-dependent current flow. Using phase-change memory (PCM) as a model system, it is shown that this "current-voltage" non-ideality arises both from the material and geometrical properties of the devices. Further, we discuss the detrimental effects of such bipolar asymmetry on in-memory matrix-vector multiply (MVM) operations and provide a scheme to compensate for it.

Compute in-Memory with Non-Volatile Elements for Neural Networks: A Review from a Co-Design Perspective

Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute-in-memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute-unit to memory, known as the von Neumann bottleneck. At its heart is a cross-bar architecture with nodal non-volatile-memory elements that performs an analog multiply-and-accumulate operation, enabling the matrix-vector-multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system-level characteristics and chip performance, including speed, power, and classification accuracy. With an over-arching co-design viewpoint, this review assesses the use of cross-bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non-volatile memory materials will need to demonstrate for a successful CIM technology.

Non-volatile resistive switching mechanism in single-layer MoS2 memristors: insights from ab initio modelling of Au and MoS2 interfaces

Non-volatile memristive devices based on two-dimensional (2D) layered materials provide an attractive alternative to conventional flash memory chips. Single-layer semiconductors, such as monolayer molybdenum disulphide (ML-MoS2), enable the aggressive downscaling of devices towards greater system integration density. The "atomristor", the most compact device to date, has been shown to undergo a resistive switching between its high-resistance (HRS) and low-resistance (LRS) states of several orders of magnitude. The main hypothesis behind its working mechanism relies on the migration of sulphur vacancies in the proximity of the metal contact during device operation, thus inducing the variation of the Schottky barrier at the metal-semiconductor interface. However, the interface physics is not yet fully understood: other hypotheses were proposed, involving the migration of metal atoms from the electrode. In this work, we aim to elucidate the mechanism of the resistive switching in the atomristor. We carry out density functional theory (DFT) simulations on model Au and ML-MoS2 interfaces with and without the presence of point defects, either vacancies or substitutions. To construct realistic interfaces, we combine DFT with Green's function surface simulations. Our findings reveal that it is not the mere presence of S vacancies but rather the migration of Au atoms from the electrode to MoS2 that modulate the interface barrier. Indeed, Au atoms act as conductive "bridges", thus facilitating the flow of charge between the two materials.

A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network

Two-terminal self-rectifying (SR)-synaptic memristors are preeminent candidates for high-density and efficient neuromorphic computing, especially for future three-dimensional integrated systems, which can self-suppress the sneak path current in crossbar arrays. However, SR-synaptic memristors face the critical challenges of nonlinear weight potentiation and steep depression, hindering their application in conventional artificial neural networks (ANNs). Here, a SR-synaptic memristor (Pt/NiO_x/WO_3-x:Ti/W) and cross-point array with sneak path current suppression features and ultrahigh-weight potentiation linearity up to 0.9997 are introduced. The image contrast enhancement and background filtering are demonstrated on the basis of the device array. Moreover, an unsupervised self-organizing map (SOM) neural network is first developed for orientation recognition with high recognition accuracy (0.98) and training efficiency and high resilience toward both noises and steep synaptic depression. These results solve the challenges of SR memristors in the conventional ANN, extending the possibilities of large-scale oxide SR-synaptic arrays for high-density, efficient, and accurate neuromorphic computing.

Graph Analysis with Multifunctional Self-Rectifying Memristive Crossbar Array

Many big data have interconnected and dynamic graph structures growing over time. Analyzing these graphical data requires the hidden relationship between the nodes in the graphs to be identified, which has conventionally been achieved by finding the effective similarity. However, graphs are generally non-Euclidean, which does not allow finding it. In this study, the non-Euclidean graphs are mapped to a specific crossbar array (CBA) composed of self-rectifying memristors and metal cells at the diagonal positions. The sneak current, an intrinsic physical property in the CBA, allows for the identification of the similarity function. The sneak-current-based similarity function indicates the distance between the nodes, which can be used to predict the probability that unconnected nodes will be connected in the future, connectivity between communities, and neural connections in a brain. When all bit lines of the CBA are connected to the ground, the sneak current is suppressed, and the CBA can be used to search for adjacent nodes. This work demonstrates the physical calculation methods applied to various graphical problems using the CBA composed of the self-rectifying memristor based on the HfO₂ switching layer. Moreover, such applications suffer less from the memristors' inherent issues related to their stochastic nature.

Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware

A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta₂ O₅ /Nb₂ O_{5- x} /Al₂ O_{3- y} /Ti CTM stack exhibiting high retention and array-level uniformity is proposed, allowing a highly reliable selector-less MCA. It shows high self-rectifying and nonlinear current-voltage characteristics below 1 µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 10⁵ s at 150 °C, suggesting the device is highly stable for non-volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self-rectifying and nonlinear characteristics allow reducing the on-chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.

Analog Tunnel Memory Based on Programmable Metallization for Passive Neuromorphic Circuits

Although experimental implementations of memristive crossbar arrays have indicated the potential of these networks for in-memory computing, their performance is generally limited by an intrinsic variability on the device level as a result of the stochastic formation of conducting filaments. A tunnel-type memristive device typically exhibits small switching variations, owing to the relatively uniform interface effect. However, the low mobility of oxygen ions and large depolarization field result in slow operation speed and poor retention. Here, we demonstrate a quantum-tunneling memory with Ag-doped percolating systems, which possesses desired characteristics for large-scale artificial neural networks. The percolating layer suppresses the random formation of conductive filaments, and the nonvolatile modulation of the Fowler-Nordheim tunneling current is enabled by the collective movement of active Ag nanocrystals with high mobility and a minimal depolarization field. Such devices simultaneously possess electroforming-free characteristics, record low switching variabilities (temporal and spatial variation down to 1.6 and 2.1%, respectively), nanosecond operation speed, and long data retention (>10⁴ s at 85 °C). Simulations prove that passive arrays with our analog memory of large current-voltage nonlinearity achieve a high write and recognition accuracy. Thus, our discovery of the unique tunnel memory contributes to an important step toward realizing neuromorphic circuits.

Ag/HfOx/Pt Unipolar Memristor for High-Efficiency Logic Operation

Unipolar resistive switching (URS) behavior, known as the SET and RESET operating in a single voltage sweep direction, has shown great potential in the simplification of the peripheral circuit. The URS memristor always involves complicated interfacial engineering and structural design. In this work, a reliable URS behavior is realized using a simple Ag/HfO_x/Pt memristor structure. The memristor displays a retention time of >10⁴ s, an ON/OFF ratio of >10³, and a good operation voltage. Synergy and competition between the Ag conductive filament formed by redox reaction and the migration of an oxygen vacancy are responsible for the observed URS. By comparison, a 35% power consumption is reduced during the logical operation from 0 to 1 to 0. The operation strategy is demonstrated by exhibiting the ACSII code of the capital letter denoted by eight logic states. This work provides a low-power concept for ultrahigh data storage using the URS memristor.

Constructing van der Waals heterostructures by dry-transfer assembly for novel optoelectronic device

Since the first successful exfoliation of graphene, the superior physical and chemical properties of two-dimensional (2D) materials, such as atomic thickness, strong in-plane bonding energy and weak inter-layer van der Waals (vdW) force have attracted wide attention. Meanwhile, there is a surge of interest in novel physics which is absent in bulk materials. Thus, vertical stacking of 2D materials could be critical to discover such physics and develop novel optoelectronic applications. Although vdW heterostructures have been grown by chemical vapor deposition, the available choices of materials for stacking is limited and the device yield is yet to be improved. Another approach to build vdW heterostructure relies on wet/dry transfer techniques like stacking Lego bricks. Although previous reviews have surveyed various wet transfer techniques, novel dry transfer techniques have been recently been demonstrated, featuring clean and sharp interfaces, which also gets rid of contamination, wrinkles, bubbles formed during wet transfer. This review summarizes the optimized dry transfer methods, which paves the way towards high-quality 2D material heterostructures with optimized interfaces. Such transfer techniques also lead to new physical phenomena while enable novel optoelectronic applications on artificial vdW heterostructures, which are discussed in the last part of this review.

A non-linear two-dimensional float gate transistor as a lateral inhibitory synapse for retinal early visual processing

Synaptic transistors that accommodate concurrent signal transmission and learning in a neural network are attracting enormous interest for neuromorphic sensory processing. To remove redundant sensory information while keeping important features, artificial synaptic transistors with non-linear conductance are desired to apply filter processing to sensory inputs. Here, we report the realization of non-linear synapses using a two-dimensional van der Waals (vdW) heterostructure (MoS₂/h-BN/graphene) based float gate memory device, in which the semiconductor channel is tailored via a surface acceptor (ZnPc) for subthreshold operation. In addition to usual synaptic plasticity, the memory device exhibits highly non-linear conductance (rectification ratio >10⁶), allowing bidirectional yet only negative/inhibitory current to pass through. We demonstrate that in a lateral coupling network, such a float gate memory device resembles the key lateral inhibition function of horizontal cells for the formation of an ON-center/OFF-surround receptive field. When combined with synaptic plasticity, the lateral inhibition weights are further tunable to enable adjustable edge enhancement for early visual processing. Our results here hopefully open a new scheme toward early sensory perception via lateral inhibitory synaptic transistors.

Sodium-Doped Titania Self-Rectifying Memristors for Crossbar Array Neuromorphic Architectures

Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for nonvolatile memory elements in artificial neural networks. However, the relatively low reliability of memristors coupled with crosstalk and sneak currents in CAAs have limited the realization of the full potential of this technology. Here, high-reliability Na-doped TiO₂  memristors grown in situ by atomic layer deposition (ALD) are demonstrated, where reversible Na migration underlies the resistive-switching mechanism. By employing ALD growth with an aqueous NaOH reactant in deionized water, uniform implantation of Na dopants is achieved in the crystallized TiO₂  thin films at 250 °C without post-annealing. The resulting Na-doped TiO₂  memristors show electroforming-free and self-rectifying resistive-switching behavior, and they are ideally suited for selectorless CAAs. Effective addressing of selectorless nodes is demonstrated via electrical measurement of individual memristors in a 6 × 6 crossbar using a read current of less than 1 µA with negligible sneak current at or below the noise level of ≈100 pA. Finally, the long-term potentiation and depression synaptic behavior from these Na-doped TiO₂  memristors achieves greater than 99.1% accuracy for image-recognition tasks using a convolutional neural network based on the selectorless of crossbar arrays.

High-Performing Self-Powered Photosensing and Reconfigurable Pyro-photoelectric Memory with Ferroelectric Hafnium Oxide

With highly diverse multifunctional properties, hafnium oxide (HfO₂ ) has attracted considerable attention not only because of its potential to address fundamental questions about material behaviors, but also its potential for applied perspectives like ferroelectric memory, transistors, and pyroelectric sensors. However, effective harvesting of the pyro-photoelectric effect of HfO₂ to develop high-performing self-biased photosensors and electric writable and optical readable memory has yet to be developed. Here, a proof-of-concept HfO₂ -based self-powered and ultrafast (response time ≈ 60 µs) infrared pyroelectric sensor with a responsivity of up to 68 µA W^-1 is developed. In particular, temporal infrared light illumination induced surface heating and, in turn, change in spontaneous polarization are attributed to robust pyro-photocurrent generation. Further, controllable suspension and reestablishment of the self-biased pyro-photocurrent response with a short electric pulse are demonstrated, which offers a conceptually new kind of photoreadable memory. Potentially, the novel approach opens a new avenue for designing on-demand pyro-phototronic response over a desired area and offers the opportunity to utilize it for various applications, including memory storage, neuromorphic vision sensors, classification, and emergency alert systems.

Printed Memtransistor Utilizing a Hybrid Perovskite/Organic Heterojunction Channel

Neuromorphic computing has the potential to address the inherent limitations of conventional integrated circuit technology, ranging from perception, pattern recognition, to memory and decision-making ( Acc. Chem. Res. 2019, 52 (4), 964-974) ( Nature 2004, 431 (7010), 796-803) ( Nat. Nanotechnol. 2013, 8 (1), 13-24). Despite their low power consumption ( Nano Lett. 2016, 16 (11), 6724-6732), traditional two-terminal memristors can perform only a single function while lacking heterosynaptic plasticity ( Nanotechnology 2013, 24 (38), 382001). Inspired by the unconditioned reflex, multiterminal memristive transistors (memtransistor) were developed to realize complex functions, such as multiterminal modulation and heterosynaptic plasticity ( Nature 2018, 554, (7693), 500-504). Here we combine a hybrid metal halide perovskite with an organic conjugated polymer to form heterojunction transistors that are responsive to both electrical and optical stimuli. We show that the synergistic effects of photoinduced ion migration in the perovskite and electronic transport in the polymer layers can be exploited to realize memristive functions. The device combines reversible, nonvolatile conductance modulation with large switching current ratios, high endurance, and long retention times. Using in situ scanning Kelvin probe microscopy and variable-temperature charge transport measurement, we correlate the collective effects of bias-induced and photoinduced ion migration with the heterosynaptic behavior observed in this hybrid memtransistor. The hybrid heterojunction channel concept is expected to be applicable to other material combinations making it a promising platform for deployment in innovative neuromorphic devices of the future.

A new opportunity for the emerging tellurium semiconductor: making resistive switching devices

The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices.

Resistive switching devices have great promise for a wide variety of technological applications. Here, Yang et al demonstrate that electrochemically induced tellurium filament can give rise to resistive switching, and show that devices based on this can provide a number of advantages compared to metallic filament-based devices.

Time-varying data processing with nonvolatile memristor-based temporal kernel

Recent advances in physical reservoir computing, which is a type of temporal kernel, have made it possible to perform complicated timing-related tasks using a linear classifier. However, the fixed reservoir dynamics in previous studies have limited application fields. In this study, temporal kernel computing was implemented with a physical kernel that consisted of a W/HfO2/TiN memristor, a capacitor, and a resistor, in which the kernel dynamics could be arbitrarily controlled by changing the circuit parameters. After the capability of the temporal kernel to identify the static MNIST data was proven, the system was adopted to recognize the sequential data, ultrasound (malignancy of lesions) and electrocardiogram (arrhythmia), that had a significantly different time constant (10-7 vs. 1 s). The suggested system feasibly performed the tasks by simply varying the capacitance and resistance. These functionalities demonstrate the high adaptability of the present temporal kernel compared to the previous ones.

Induced Complementary Resistive Switching in Forming-Free TiOx/TiO2/TiOx Memristors

The undesirable sneak current path is one of the key challenges in high-density memory integration for the emerging cross-bar memristor arrays. This work demonstrates a new heterojunction design of oxide multilayer stacking with different oxygen vacancy contents to manipulate the oxidation state. We show that the bipolar resistive switching (BRS) behavior of the Pt/TiO_x/Pt cross-bar structure can be changed to complementary resistive switching (CRS) by introducing a thin TiO₂ layer in the middle of the TiO_x layer to obtain a Pt/TiO_x/TiO₂/TiO_x/Pt device architecture with a double-junction active matrix. In contrast to the BRS in a single-layer TiO_x matrix, the device with a double-junction matrix remains in a high-resistance state in the voltage range below the SET voltage, which makes it an efficient structure to overcome the sneak path constraints of undesired half-selected cells that lead to incorrect output reading. This architecture is capable of eliminating these half-selected cells between the nearby cross-bar cells in a smaller programming voltage range. A simplified model for the switching mechanism can be used to account for the observed high-quality switching performance with excellent endurance and current retention properties.

Low-Power Memristive Logic Device Enabled by Controllable Oxidation of 2D HfSe2 for In-Memory Computing

Memristive logic device is a promising unit for beyond von Neumann computing systems and 2D materials are widely used because of their controllable interfacial properties. Most of these 2D memristive devices, however, are made from semiconducting chalcogenides which fail to gate the off-state current. To this end, a crossbar device using 2D HfSe2 is fabricated, and then the top layers are oxidized into "high-k" dielectric HfSex Oy via oxygen plasma treatment, so that the cell resistance can be remarkably increased. This two-terminal Ti/HfSex Oy /HfSe2 /Au device exhibits excellent forming-free resistive switching performance with high switching speed (<50 ns), low operation voltage (<3 V), large switching window (103 ), and good data retention. Most importantly, the operation current and the power consumption reach 100 pA and 0.1 fJ to 0.1 pJ, much lower than other HfO based memristors. A functionally complete low-power Boolean logic is experimentally demonstrated using the memristive device, allowing it in the application of energy-efficient in-memory computing.

Self-rectifying resistive memory in passive crossbar arrays

Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf0.8Si0.2O2/Al2O3/Hf0.5Si0.5O2)-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 104), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency (<10 μs), (v) excellent non-volatility (data retention >104 s at 85 °C), and (vi) complementary metal-oxide-semiconductor compatibility (maximum supply voltage ≤5 V) is introduced, which outperforms previously reported SRMCs. These characteristics render the SRMC highly suitable for the main memory for memory-centric computing which can improve deep learning acceleration. Furthermore, the low programming power (ca. 18 nW), latency (100 μs), and endurance (>106) highlight the energy-efficiency and highly reliable random-access memory of our SRMC. The feasible operation of individual SRMCs in passive crossbar arrays of different sizes (30 × 30, 160 × 160, and 320 × 320) is attributed to the large asymmetry and nonlinearity in the current-voltage behavior of the proposed SRMC, verifying its potential for application in large-scale and high-density non-volatile memory for memory-centric computing.

Reversible Barrier Switching of ZnO/RuO2 Schottky Diodes

The current-voltage characteristics of ZnO/RuO2 Schottky diodes prepared by magnetron sputtering are shown to exhibit a reversible hysteresis behavior, which corresponds to a variation of the Schottky barrier height between 0.9 and 1.3 eV upon voltage cycling. The changes in the barrier height are attributed to trapping and de-trapping of electrons in oxygen vacancies.

High-Stability Memristive Devices Based on Pd Conductive Filaments and Its Applications in Neuromorphic Computing

Memristive devices with high-density and high-speed performance have considerable potential for neuromorphic computing applications in data storage and artificial synapses. However, current memristive devices that are based on conductive filaments, such as silver, are unstable owing to the high mobility and low thermodynamic stability of the filaments. A high-quality SnSe film was deposited using the pulsed laser deposition technology, and high-performance Pd/SnSe/NSTO devices were fabricated. High-stability memristive devices can not only implement simple arithmetic function but also exhibit the centralized distribution of SET/RESET voltage and cell-cell uniformity. The SET/RESET power can achieve approximately 4.1 and 61 μW power. The possibility of Pd filament formation and Pd²⁺ diffusion in SnSe thin films is first confirmed by combining high-resolution transmission electron microscopy, energy-dispersive spectrometer mapping, and first principle calculation. The formation and destruction process of Pd filaments can simulate the influx and extrusion kinetics of K⁺, Ca²⁺, or Na⁺ in biological synapses and implements considerable synaptic functions. This study thus provides a new idea for improving device performance using different filament materials, which can greatly facilitate the development of neuromorphic computing.

Memristive Quantized Neural Networks: A Novel Approach to Accelerate Deep Learning On-Chip

Existing deep neural networks (DNNs) are computationally expensive and memory intensive, which hinder their further deployment in novel nanoscale devices and applications with lower memory resources or strict latency requirements. In this paper, a novel approach to accelerate on-chip learning systems using memristive quantized neural networks (M-QNNs) is presented. A real problem of multilevel memristive synaptic weights due to device-to-device (D2D) and cycle-to-cycle (C2C) variations is considered. Different levels of Gaussian noise are added to the memristive model during each adjustment. Another method of using memristors with binary states to build M-QNNs is presented, which suffers from fewer D2D and C2C variations compared with using multilevel memristors. Furthermore, methods of solving the sneak path issues in the memristive crossbar arrays are proposed. The M-QNN approach is evaluated on two image classification datasets, that is, ten-digit number and handwritten images of mixed National Institute of Standards and Technology (MNIST). In addition, input images with different levels of zero-mean Gaussian noise are tested to verify the robustness of the proposed method. Another highlight of the proposed method is that it can significantly reduce computational time and memory during the process of image recognition.

A Battery-Like Self-Selecting Biomemristor from Earth-Abundant Natural Biomaterials

Using the earth-abundant natural biomaterials to manufacture functional electronic devices meets the sustainable requirement of green electronics, especially for the practical application of memristors in data storage and neuromorphic computing. However, the sneak currents flowing though the unselected cells in a large-scale cross-bar memristor array is one of the major problems which need to be tackled. The self-selecting memristors can solve the problem to develop compact and concise integrated circuits. Here, a sustainable natural biomaterial (anthocyanin, C₁₅H₁₁O₆) extracted from plant tissue is demonstrated for ions and electron transport. The capacitive-coupled memristive behavior of as-prepared bioelectronic device can be significantly modulated by diethylmethyl(2-methoxyethyl)ammoium bis(trifluoromethylsulfonyl)imide (DEME-TFSI) ionic liquid (IL). Furthermore, graphene was inserted into biomaterial matrix to manipulate the memristive effects by graphene protonation. This results in a battery-like self-selective memristive effect. This phenomenon is explained by a physical model and density functional theory (DFT) based first-principles calculations. Finally, the self-selective behavior was applied in 0T-1R array configuration, which indicates the battery-like self-selecting biomemristor has potential applications in the brain-inspired computing, data storage systems, and high-density device integration.

Nano-engineering and functionalization of hybrid Au-MexOy-TiO2 (Me = W, Ga) hetero-interfaces for optoelectronic receptors and nociceptors

Bio-inspired nano-electronic devices are key instruments for the development of advanced artificial intelligence systems, which will shape the future of humanoid nano-robotics. An emerging demand is realized for an accurate reception of environmental stimuli via visual perception, processing and realization of optical signals. The present study demonstrates the capability of functionalized all-oxide heterostructured two-dimensional (2D) plasmonic devices for the self-adaptive recognition of visual optical pulses. Specifically, the nano-engineering of the metal/semiconductor interface and co-modulation of heterostructured 2D semiconductor hetero-interfaces of Au/WO3 : TiO2 and Au/Ga2O3 : TiO2 facilitated the receptive and nociceptive detection of visible light pulses. A decrease in the dark current of the Au/WO3 : TiO2 unit resulted in the development of sensitive visible light photoreceptors. Furthermore, the modulation of charge transfers at the Au/Ga2O3 : TiO2 hetero-interfaces were the key parameter to determine the optical reception characteristics and nociceptive performance of all-oxide optoelectronic devices. Specifically, the rapid thermal annealing (RTA) of 2D Ga2O3 in N2 atmosphere ensured the modulation of charge transfer at Au/Ga2O3 : TiO2 hetero-interfaces in plasmonic devices. Thus, hetero-interface engineering enabled the effective control of charge transfer at 2D hetero-interfaces for an adaptive perception of visible optical pulses. Consequently, the fabricated sensitive Au/Ga2O3 (N2) : TiO2 bio-inspired unit emulated the optical functionalities of corneal nociceptors.

Lead-Free Dual-Phase Halide Perovskites for Preconditioned Conducting-Bridge Memory

Organometallic and all-inorganic halide perovskites (HPs) have recently emerged as promising candidate materials for resistive switching (RS) nonvolatile memory due to their current-voltage hysteresis caused by fast ion migration. Lead-free and all-inorganic HPs have been researched for non-toxic and environmentally friendly RS memory devices. However, only HP-based devices with electrochemically active top electrode (TE) exhibit ultra-low operating voltages and high on/off ratio RS properties. The active TE easily reacts to halide ions in HP films, and the devices have a low device durability. Herein, RS memory devices based on an air-stable lead-free all-inorganic dual-phase HP (AgBi₂ I₇ -Cs₃ Bi₂ I₉ ) are successfully fabricated with inert metal electrodes. The devices with Au TE show filamentary RS behavior by conducting-bridge involving Ag cations in HPs with ultra-low operating voltages (<0.15 V), high on/off ratio (>10⁷ ), multilevel data storage, and long retention times (>5 × 10⁴ s). The use of a closed-loop pulse switching method improves reversible RS properties up to 10³ cycles with high on/off ratio above 10⁶ . With an extremely small bending radius of 1 mm, the devices are operable with reasonable RS characteristics. This work provides a promising material strategy for lead-free all-inorganic HP-based nonvolatile memory devices for practical applications.

Nanometer-Scale Uniform Conductance Switching in Molecular Memristors

One common challenge highlighted in almost every review article on organic resistive memory is the lack of areal switching uniformity. This, in fact, is a puzzle because a molecular switching mechanism should ideally be isotropic and produce homogeneous current switching free from electroforming. Such a demonstration, however, remains elusive to date. The reports attempting to characterize a nanoscopic picture of switching in molecular films show random current spikes, just opposite to the expectation. Here, this longstanding conundrum is resolved by demonstrating 100% spatially homogeneous current switching (driven by molecular redox) in memristors based on Ru-complexes of azo-aromatic ligands. Through a concurrent nanoscopic spatial mapping using conductive atomic force microscopy and in operando tip-enhanced Raman spectroscopy (both with resolution <7 nm), it is shown that molecular switching in the films is uniform from hundreds of micrometers down to the nanoscale and that conductance value exactly correlates with spectroscopically determined molecular redox states. This provides a deterministic molecular route to obtain spatially homogeneous, forming-free switching that can conceivably overcome the chronic problems of robustness, consistency, reproducibility, and scalability in organic memristors.

Synthesis and Memristor Effect of a Forming-Free ZnO Nanocrystalline Films

We experimentally investigated the effect of post-growth annealing on the morphological, structural, and electrophysical parameters of nanocrystalline ZnO films fabricated by pulsed laser deposition. The influence of post-growth annealing modes on the electroforming voltage and the resistive switching effect in ZnO nanocrystalline films is investigated. We demonstrated that nanocrystalline zinc oxide films, fabricated at certain regimes, show the electroforming-free resistive switching. It was shown, that the forming-free nanocrystalline ZnO film demonstrated a resistive switching effect and switched at a voltage 1.9 ± 0.2 V from 62.42 ± 6.47 (RHRS) to 0.83 ± 0.06 kΩ (RLRS). The influence of ZnO surface morphology on the resistive switching effect is experimentally investigated. It was shown, that the ZnO nanocrystalline film exhibits a stable resistive switching effect, which is weakly dependent on its nanoscale structure. The influence of technological parameters on the resistive switching effect in a forming-free ZnO nanocrystalline film is investigated. The results can be used for fabrication of new-generation micro- and nanoelectronics elements, including random resistive memory (ReRAM) elements for neuromorphic structures based on forming-free ZnO nanocrystalline films.

Research progress on solutions to the sneak path issue in memristor crossbar arrays

Since the emergence of memristors (or memristive devices), how to integrate them into arrays has been widely investigated. After years of research, memristor crossbar arrays have been proposed and realized with potential applications in nonvolatile memory, logic and neuromorphic computing systems. Despite the promising prospects of memristor crossbar arrays, one of the main obstacles for their development is the so-called sneak-path current causing cross-talk interference between adjacent memory cells and thus may result in misinterpretation which greatly influences the operation of memristor crossbar arrays. Solving the sneak-path current issue, the power consumption of the array will immensely decrease, and the reliability and stability will simultaneously increase. In order to suppress the sneak-path current, various solutions have been provided. So far, some reviews have considered some of these solutions and established a sophisticated classification, including 1D1M, 1T1M, 1S1M (D: diode, M: memristor, T: transistor, S: selector), self-selective and self-rectifying memristors. Recently, a mass of studies have been additionally reported. This review thus attempts to provide a survey on these new findings, by highlighting the latest research progress realized for relieving the sneak-path issue. Here, we first present the concept of the sneak-path current issue and solutions proposed to solve it. Consequently, we select some typical and promising devices, and present their structures and properties in detail. Then, the latest research activities focusing on single-device structures are introduced taking into account the mechanisms underlying these devices. Finally, we summarize the properties and perspectives of these solutions.

Silicon oxide-niobium oxide mixture films and nanolaminates grown by atomic layer deposition from niobium pentaethoxide and hexakis(ethylamino) disilane

Amorphous SiO₂-Nb₂O₅ nanolaminates and mixture films were grown by atomic layer deposition. The films were grown at 300 °C from Nb(OC₂H₅)₅, Si₂(NHC₂H₅)₆, and O₃ to thicknesses ranging from 13 to 130 nm. The niobium to silicon atomic ratio was varied in the range of 0.11-7.20. After optimizing the composition, resistive switching properties could be observed in the form of characteristic current-voltage behavior. Switching parameters in the conventional regime were well defined only in a SiO₂:Nb₂O₅ mixture at certain, optimized, composition with Nb:Si atomic ratio of 0.13, whereas low-reading voltage measurements allowed recording memory effects in a wider composition range.

Nanoscale All-Oxide-Heterostructured Bio-inspired Optoresponsive Nociceptor

Retina nociceptor, as a key sensory receptor, not only enables the transport of warning signals to the human central nervous system upon its exposure to noxious stimuli, but also triggers the motor response that minimizes potential sensitization. In this study, the capability of two-dimensional all-oxide-heterostructured artificial nociceptor as a single device with tunable properties was confirmed. Newly designed nociceptors utilize ultra-thin sub-stoichiometric TiO₂-Ga₂O₃ heterostructures, where the thermally annealed Ga₂O₃ films play the role of charge transfer controlling component. It is discovered that the phase transformation in Ga₂O₃ is accompanied by substantial jump in conductivity, induced by thermally assisted internal redox reaction of Ga₂O₃ nanostructure during annealing. It is also experimentally confirmed that the charge transfer in all-oxide heterostructures can be tuned and controlled by the heterointerfaces manipulation. Results demonstrate that the engineering of heterointerfaces of two-dimensional (2D) films enables the fabrication of either high-sensitive TiO₂-Ga₂O₃ (Ar) or high-threshold TiO₂-Ga₂O₃ (N₂) nociceptors. The hypersensitive nociceptor mimics the functionalities of corneal nociceptors of human eye, whereas the delayed reaction of nociceptor is similar to high-threshold nociceptive characteristics of human sensory system. The long-term stability of 2D nociceptors demonstrates the capability of heterointerfaces engineering for effective control of charge transfer at 2D heterostructured devices.

Defect-Engineered Electroforming-Free Analog HfOx Memristor and Its Application to the Neural Network

The thin-film growth conditions in a plasma-enhanced atomic layer deposition for the (3.0-4.5) nm thick HfO₂ film were optimized to use the film as the resistive switching element in a neuromorphic circuit. The film was intended to be used as a feasible synapse with analog-type conductance-tuning capability. The 4.5 nm thick HfO₂ films on both conventional TiN and a new RuO₂ bottom electrode required the electroforming process for them to operate as a feasible resistive switching memory, which was the primary source of the undesirable characteristics as the synapse. Therefore, electroforming-free performance was necessary, which could be accomplished by thinning the HfO₂ film down to 3.0 nm. However, the device with only the RuO₂ bottom electrode offered the desired functionality without involving too high leakage or shorting problems, which are due to the recovery of the stoichiometric composition of the HfO₂ near the RuO₂ layer. In conjunction with the Ta top electrode, which provided the necessary oxygen vacancies to the HfO₂ layer, and the high functionality of the RuO₂ as the scavenger of excessive incorporated oxygen vacancies, which appeared to be inevitable during the repeated switching operation, the Ta/3.0 nm HfO₂/RuO₂ provided a highly useful synaptic device component in the neuromorphic hardware system.

Programmable, electroforming-free TiOx/TaOx heterojunction-based non-volatile memory devices

Electroforming-free resistive switching in memristors is essential to reliably achieving the performance of high switching speed, high endurance, good signal retention, and low power consumption expected for next-generation computing devices. Although there have been various approaches to resolve the issues observed with the electroforming process in oxide-based memory devices, most of them end up having high SET and RESET voltages and short lifetimes. We present a heterojunction interface of oxygen-vacancy-defect-rich ultrananocrystalline TiOx and TaOx films used as the switching matrix, which enables high-quality electroforming-free switching with a much lower programming voltage (+0.5-0.8 V), a high endurance of over 104 cycles and good retention performance with an estimated device lifetime of over 10 years. The electroforming-free switching behavior is governed by migration of oxygen vacancies driven by electric field localization that is imposed by the ultrananocrystalline nature of the TaOx film, serving as the switching matrix, with the TiOx film serving as an additional oxygen vacancy source to reduce the overall resistivity of TaOx and provide low-bias rectification. The ability to perform electroforming-free resistive switching along with excellent switching repeatability and retention capabilities for various digital and analog programmable voltages enables high scalability and large density integration of the cross-bar ReRAM framework.

Tin oxide artificial synapses for low power temporal information processing

Lateral memristors configured with inert Pt contacts and mixed phase tin oxide layers have exhibited immediate, forming-free, low-power bidirectional resistance switching. Activity dependent conductance and relaxation in the low resistance state resembled short term potentiation in biological synapses. After scanning probe microscopy, x-ray photoelectron spectroscopy and electrical measurements, the device characteristics were attributed to Joule heating induced decomposition of the minority SnO phase and formation of a SnO₂ conducting filament with higher effective n-type doping. Finally, the devices recognized input voltage pulse sequences and spectral data by returning unique conductance states, suggesting suitability for bio-inspired pattern recognition systems.

Simultaneous implementation of resistive switching and rectifying effects in a metal-organic framework with switched hydrogen bond pathway

Resistive random-access memory (RRAM) has evolved as one of the most promising candidates for the next-generation memory, but bistability for information storage, simultaneous implementation of resistive switching and rectification effects, and a better understanding of switching mechanism are still challenging in this field. Herein, we report a RRAM device based on a chiral metal-organic framework (MOF) FJU-23-H₂O with switched hydrogen bond pathway within its channels, exhibiting an ultralow set voltage (~0.2 V), a high ON/OFF ratio (~10⁵), and a high rectification ratio (~10⁵). It is not only the first MOF with voltage-gated proton conduction but also the first single material showing both rectifying and resistive switching effects. By single-crystal x-ray diffraction analyses, the mechanism of the resistive switching has been demonstrated.

Self-selective van der Waals heterostructures for large scale memory array

The large-scale crossbar array is a promising architecture for hardware-amenable energy efficient three-dimensional memory and neuromorphic computing systems. While accessing a memory cell with negligible sneak currents remains a fundamental issue in the crossbar array architecture, up-to-date memory cells for large-scale crossbar arrays suffer from process and device integration (one selector one resistor) or destructive read operation (complementary resistive switching). Here, we introduce a self-selective memory cell based on hexagonal boron nitride and graphene in a vertical heterostructure. Combining non-volatile and volatile memory operations in the two hexagonal boron nitride layers, we demonstrate a self-selectivity of 10¹⁰ with an on/off resistance ratio larger than 10³. The graphene layer efficiently blocks the diffusion of volatile silver filaments to integrate the volatile and non-volatile kinetics in a novel way. Our self-selective memory minimizes sneak currents on large-scale memory operation, thereby achieving a practical readout margin for terabit-scale and energy-efficient memory integration.

Designing large-scale crossbar arrays for energy efficient neuromorphic computing systems remains a challenge. Here, the authors propose Van der Waals (h-BN/graphene/h-BN) self-selective memory design able to combine, in the same cell, non-volatile and volatile behaviors with negligible sneak current.

A Highly Transparent Artificial Photonic Nociceptor

A nociceptor is an essential element in the human body, alerting us to potential damage from extremes in temperature, pressure, etc. Realizing nociceptive behavior in an electronics device remains a central issue for researchers, designing neuromorphic devices. This study proposes and demonstrates an all-oxide-based highly transparent ultraviolet-triggered artificial nociceptor, which responds in a very similar way to the human eye. The device shows a high transmittance (>65%) and very low absorbance in the visible region. The current-voltage characteristics show loop opening, which is attributed to the charge trapping/detrapping. Further, the ultraviolet-stimuli-induced versatile criteria of a nociceptor such as a threshold, relaxation, allodynia, and hyperalgesia are demonstrated under self-biased condition, providing an energy-efficient approach for the neuromorphic device operation. The reported optically controlled features open a new avenue for the development of transparent optoelectronic nociceptors, artificial eyes, and memory storage applications.