Confining Cation Injection to Enhance CBRAM Performance by Nanopore Graphene Layer

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

Kinetic Monte Carlo simulations on electroforming in nanomanipulated conductive bridge random access memory devices

Conductive bridge random access memory (CBRAM) devices exhibit great potential as the next-generation nonvolatile memory devices. However, they suffer from two major disadvantages, namely relatively high power consumption and large cycle-to-cycle and device-to-device variations, which hinder their more extensive commercialization. To learn how to enhance their device performance, kinetic Monte Carlo (KMC) simulations were employed to illustrate the variation of electroforming processes in nanomanipulated CBRAM devices by introducing an ion-blocking layer with scalable nanopores and tuning the microstructures of dielectric layers. Both the size of nanopores and the inhomogeneity of dielectric layers have significant impacts on the forming processes of conductive filaments. The dielectric layer with a high-content loose texture plus the scalable nanopore-containing ion-blocking layer leads to the formation of size-controlled and uniform filaments, which remarkably contributes to miniaturizable and stable CBRAM devices. Our study provides insights into nanomanipulation strategies to realize high-performance CBRAM devices, still awaiting future experimental confirmation.

Fully printed memristors made with MoS2 and graphene water-based inks

2-Dimensional materials (2DMs) offer an attractive solution for the realization of high density and reliable memristors, compatible with printed and flexible electronics. In this work we fabricate a fully inkjet printed MoS2-based resistive switching memory, where graphene is used as top electrode and silver is used as bottom electrode. Memristic effects are observed only after annealing of each printed component. The printed memory on silicon shows low SET/RESET voltage, short switching times (less than 0.1 s) and resistance switching ratios of 103-105, comparable or superior to the performance obtained in devices with both printed silver electrodes on rigid substrates. The same device on Kapton shows resistance switching ratios of 102-103 and remains stable at least up to 2% of strain. The memristor resistance switching is attributed to the formation of Ag conductive filaments, which can be suppressed by integrating graphene grown by chemical vapour deposition (CVD) onto the silver electrode. Temperature-dependent electrical measurements starting from 200 K show that memristic behavior appears at a temperature of ∼300 K, confirming that an energy threshold is needed to form the conductive filament. This work shows that inkjet printing is a very powerful technique for the fabrication of 2DMs-based resistive switches onto rigid and flexible substrates.

Formation of Cluster-Structured Metallic Filaments in Organic Memristors for Wearable Neuromorphic Systems with Bio-Mimetic Synaptic Weight Distributions

With increasing demand for wearable electronics capable of computing huge data, flexible neuromorphic systems mimicking brain functions have been receiving much attention. Despite considerable efforts in developing practical neural networks utilizing several types of flexible artificial synapses, it is still challenging to develop wearable systems for complex computations due to the difficulties in emulating continuous memory states in a synaptic component. In this study, polymer conductivity is analyzed as a crucial factor in determining the growth dynamics of metallic filaments in organic memristors. Moreover, flexible memristors with bio-mimetic synaptic functions such as linearly tunable weights are demonstrated by engineering the polymer conductivity. In the organic memristor, the cluster-structured filaments are grown within the polymer medium in response to electric stimuli, resulting in gradual resistive switching and stable synaptic plasticity. Additionally, the device exhibits the continuous and numerous non-volatile memory states due to its low leakage current. Furthermore, complex hardware neural networks including ternary logic operators and a noisy image recognitions system are successfully implemented utilizing the developed memristor arrays. This promising concept of creating flexible neural networks with bio-mimetic weight distributions will contribute to the development of a new computing architecture for energy-efficient wearable smart electronics.

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.

Highly Stable HfO2 Memristors through van der Waals Electrode Lamination and Delamination

Memristors have attracted considerable attention in the past decade, holding great promise for future neuromorphic computing. However, the intrinsic poor stability and large device variability remain key limitations for practical application. Here, we report a simple method to directly visualize the origin of poor stability. By mechanically removing the top electrodes of memristors operated at different states (such as SET or RESET), the memristive layer could be exposed and directly characterized through conductive atomic force microscopy, providing two-dimensional area information within memristors. Based on this technique, we observed the existence of multiple conducting filaments during the formation process and built up a physical model between filament numbers and the cycle-to-cycle variation. Furthermore, by improving the interface quality through the van der Waals top electrode, we could reduce the filament number down to a single filament during all switching cycles, leading to much controlled switching behavior and reliable device operation.

Definition of a Localized Conducting Path via Suppressed Charge Injection in Oxide Memristors for Stable Practical Hardware Neural Networks

Oxide-based memristors have been demonstrated as suitable options for memory components in neuromorphic systems. In such devices, the resistive switching characteristics are caused by the formation of conductive filaments (CFs) comprising oxygen vacancies. Thus, the electrical performance is primarily governed by the CF structure. Despite various approaches for regulating the oxygen vacancy distributions in oxide memristors, controlling the CF structure without modifying the device configuration related to material compatibility is still a challenge. This study demonstrates an effective strategy for localizing CF distributions in memristors by suppressing charge injection during the formation of conducting paths. As the injected charge quantity is reduced in the electroforming process of the oxide memristor, the CF distributions become narrower, leading to more reproducible and stable resistive switching characteristics in the device. Based on these findings, a reliable hardware neural network comprising oxide memristors is constructed to recognize complex images. The developed memristor has been employed as a synaptic memory component in systems without degradation for a long time. This promising concept of oxide memristors acting as stable synaptic components holds great potential for developing practical neuromorphic systems and their expansion into artificial intelligent systems.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

Self-Compliant Threshold Switching Devices with High On/Off ratio by Control of Quantized Conductance in Ag Filaments

Threshold switches based on conductive metal bridge devices are useful as selectors to block sneak leakage paths in memristor arrays used in neuromorphic computing and emerging nonvolatile memory. We demonstrate that control of Ag-cation concentration in Al₂O₃ electrolyte and Ag filament size and density play an important role in the high on/off ratio and self-compliance of metal-ion-based volatile threshold switching devices. To control Ag-cation diffusion, we inserted an engineered defective graphene monolayer between the Ag electrode and the Al₂O₃ electrolyte. The Ag-cation migration and the Ag filament size and density are limited by the pores in the defective graphene monolayer. This leads to quantized conductance in the Ag filaments and self-compliance resulting from the formation and dissolution of the Ag conductive filament.

Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing

The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.

Rational Design on Polymorphous Phase Switching in Molybdenum Diselenide-Based Memristor Assisted by All-Solid-State Reversible Intercalation toward Neuromorphic Application

In this work, a low-power memristor based on vertically stacked two-dimensional (2D) layered materials, achieved by plasma-assisted vapor reaction, as the switching material, with which the copper and gold metals as electrodes featured by reversible polymorphous phase changes from a conducting 1T-phase to a semiconducting 2H-one once copper cations interacted between vertical lamellar layers and vice versa, was demonstrated. Here, molybdenum diselenide was chosen as the switching material, and the reversible polymorphous phase changes activated by the intercalation of Cu cations were confirmed by pseudo-operando Raman scattering, transmission electron microscopy, and scanning photoelectron microscopy under high and low resistance states, respectively. The switching can be activated at about ±1 V with critical currents less than 10 μA with an on/off ratio approaching 100 after 100 cycles and low power consumption of ∼0.1 microwatt as well as linear weight updates controlled by the amount of intercalation. The work provides alternative feasibility of reversible and all-solid-state metal interactions, which benefits monolithic integrations of 2D materials into operative electronic circuits.

Ultralow Set Voltage and Enhanced Switching Reliability for Resistive Random-Access Memory Enabled by an Electrodeposited Nanocone Array

Resistive random-access memory (RRAM) has been extensively investigated for 20 years due to its excellent advantages, including scalability, switching speed, compatibility with the complementary metal oxide semiconductor process, and neuromorphic computing application. However, the issue of memristor reliability for cycle to cycle and device to device resulting from the random ion drift and diffusion in solid-state thin films is still a great challenge for commercialization. Therefore, controlling the internal ionic process to improve the memristor performance and reliability is a primary and urgent task. Here, a Ni nanocone array prepared by an electrodeposition method is integrated with an Ag/Al₂O₃/Pt resistive switching device. The nanocone-array-based memristor yields superior switching performance, including an ultralow set voltage (-0.37 V), a concentrated voltage/resistance distribution (C_V 14.8%/32.7%), robust endurance (>10⁵ cycles), and multilevel storage capability. A finite element analysis, transmission electron microscope observation, and current mapping test indicate that the local enhancement of the electric field confines the ionic migration process and yields a predictable formation and dissolution process of the conductive filament. The nanocone-array-based RRAM device provides a new and feasible method to control the conductive filament growth reliably, which paves the way for memristor development.

A Robust Memristor Based on Epitaxial Vertically Aligned Nanostructured BaTiO3 -CeO2 Films on Silicon

With the exploration of ferroelectric materials, researchers have a strong desire to explore the next generation of non-volatile ferroelectric memory with silicon-based epitaxy, high-density storage, and algebraic operations. Herein, a silicon-based memristor with an epitaxial vertically aligned nanostructures BaTiO₃ -CeO₂ film based on La_0.67 Sr_0.33 MnO₃ /SrTiO₃ /Si substrate is reported. The ferroelectric polarization reversal is optimized through the continuous exploring of growth temperature, and the epitaxial structure is obtained, thus it improves the resistance characteristic, the multi-value storage function of five states is achieved, and the robust endurance characteristic can reach 10⁹ cycles. In the synapse plasticity modulated by pulse voltage process, the function of the spiking-time-dependent plasticity and paired-pulse facilitation is simulated successfully. More importantly, the algebraic operations of addition, subtraction, multiplication, and division are realized by using fast speed pulse of the width ≈50 ns. Subsequently, a convolutional neural network is constructed for identifying the CIFAR-10 dataset, to simulate the performance of the device; the online and offline learning recognition rate reach 90.03% and 92.55%, respectively. Overall, this study paves the way for memristors with silicon-based epitaxial ferroelectric films to realize multi-value storage, algebraic operations, and neural computing chip applications.

Hafnium Oxide (HfO2 ) - A Multifunctional Oxide: A Review on the Prospect and Challenges of Hafnium Oxide in Resistive Switching and Ferroelectric Memories

Hafnium oxide (HfO₂ ) is one of the mature high-k dielectrics that has been standing strong in the memory arena over the last two decades. Its dielectric properties have been researched rigorously for the development of flash memory devices. In this review, the application of HfO₂ in two main emerging nonvolatile memory technologies is surveyed, namely resistive random access memory and ferroelectric memory. How the properties of HfO₂ equip the former to achieve superlative performance with high-speed reliable switching, excellent endurance, and retention is discussed. The parameters to control HfO₂ domains are further discussed, which can unleash the ferroelectric properties in memory applications. Finally, the prospect of HfO₂ materials in emerging applications, such as high-density memory and neuromorphic devices are examined, and the various challenges of HfO₂ -based resistive random access memory and ferroelectric memory devices are addressed with a future outlook.

Mimicking Neuroplasticity via Ion Migration in van der Waals Layered Copper Indium Thiophosphate

Artificial synaptic devices are the essential components of neuromorphic computing systems, which are capable of parallel information storage and processing with high area and energy efficiencies, showing high promise in future storage systems and in-memory computing. Analogous to the diffusion of neurotransmitter between neurons, ion-migration-based synaptic devices are becoming promising for mimicking synaptic plasticity, though the precise control of ion migration is still challenging. Due to the unique 2D nature and highly anisotropic ionic transport properties, van der Waals layered materials are attractive for synaptic device applications. Here, utilizing the high conductivity from Cu⁺ -ion migration, a two-terminal artificial synaptic device based on layered copper indium thiophosphate is studied. By controlling the migration of Cu⁺ ions with an electric field, the device mimics various neuroplasticity functions, such as short-term plasticity, long-term plasticity, and spike-time-dependent plasticity. The Pavlovian conditioning and activity-dependent synaptic plasticity involved neural functions are also successfully emulated. These results show a promising opportunity to modulate ion migration in 2D materials through field-driven ionic processes, making the demonstrated synaptic device an intriguing candidate for future low-power neuromorphic applications.

Reliable Memristor Based on Ultrathin Native Silicon Oxide

Memristors based on two-dimensional (2D) materials can exhibit great scalability and ultralow power consumption, yet the structural and thickness inhomogeneity of ultrathin electrolytes lowers the production yield and reliability of devices. Here, we report that the self-limiting amorphous SiO_x (∼2.7 nm) provides a perfect atomically thin electrolyte with high uniformity, featuring a record high production yield. With the guidance of physical modeling, we reveal that the atomic thickness of SiO_x enables anomalous resistive switching with a transition to an analog quasi-reset mode, where the filament stability can be further enhanced using Ag-Au nanocomposite electrodes. Such a picojoule memristor shows record low switching variabilities (C2C and D2D variation down to 1.1 and 2.6%, respectively), good retention at a few microsiemens, and high conductance-updating linearity, constituting key metrics for analog neural networks. In addition, the stable high-resistance state is found to be an excellent source for true random numbers of Gaussian distribution. This work opens up opportunities in mass production of Si-compatible memristors for ultradense neuromorphic and security hardware.

Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

Conductive-bridging random access memory (CBRAM) has garnered attention as a building block of non-von Neumann architectures because of scalability and parallel processing on the crossbar array. To integrate CBRAM into the back-end-of-line (BEOL) process, amorphous switching materials have been investigated for practical usage. However, both the inherent randomness of filaments and disorders of amorphous material lead to poor reliability. In this study, a highly reliable nanoporous-defective bottom layer (NP-DBL) structure based on amorphous TiO₂ is demonstrated (Ag/a-TiO₂/a-TiO_x/p-Si). The stoichiometries of DBL and the pore size can be manipulated to achieve the analog conductance updates and multilevel conductance by 300 states with 1.3% variation, and 10 levels, respectively. Compared with nonporous TiO₂ CBRAM, endurance, retention, and uniformity can be improved by 10⁶ pulses, 28 days at 85°C, and 6.7 times, respectively. These results suggest even amorphous-based systems, elaborately tuned structural variables, can help design more reliable CBRAMs.

Rational Design on Controllable Cation Injection with Improved Conductive-Bridge Random Access Memory by Glancing Angle Deposition Technology toward Neuromorphic Application

A conductive-bridge random access memory (CBRAM) has been considered a promising candidate for the next-generation nonvolatile memory technology because of its excellent performance, for which the resistive switching behavior depends on the formation/dissolution of conducting filaments in an electrolyte layer originated by the cation injection from the active electrode with electrochemical reactions. Typically, the controllability of cations into the electrolyte layer is a main issue, leading to stable switching reliability. In this work, an architecture combining spike-shaped Ag electrodes created by Al₂O₃ nanopillar arrays as a physical diffusion barrier by glancing angle deposition technology was proposed to localize Ag cation injection for the formation of controllable filaments inside TiO_x as the switching layer. Interestingly, the dimension of the Ag plugs defined by the topography of Al₂O₃ nanopillar arrays can control Ag cation injection to influence the dimensionality of conductive filaments. Compared to the typical planar-Ag/TiO_x/Pt device, the spiked-Ag/Al₂O₃ nanopillar arrays/TiO_x/Pt device shows improvement of endurance and voltage disturbance. With enhanced multilevel characteristics, the spiked active-metal-based CBRAM device can be expected to serve as an analogue synapse for neuromorphic applications.

Standards for the Characterization of Endurance in Resistive Switching Devices

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >10⁶ cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

Nanomaterials and their applications on bio-inspired wearable electronics

Wearable electronics featuring conformal attachment, sensitive perception and intellectual signal processing have made significant progress in recent years. However, when compared with living organisms, artificial sensory devices showed undeniable bulky shape, poor adaptability, and large energy consumption. To make up for the deficiencies, biological examples provide inspirations of novel designs and practical applications. In the field of biomimetics, nanomaterials from nanoparticles to layered two-dimensional materials are actively involved due to their outstanding physicochemical properties and nanoscale configurability. This review focuses on nanomaterials related to wearable electronics through bioinspired approaches on three different levels, interfacial packaging, sensory structure, and signal processing, which comprehensively guided recent progress of wearable devices in leveraging both nanomaterial superiorities and biorealistic functionalities. In addition, opinions on potential development trend are proposed aiming at implementing bioinspired electronics in multifunctional portable sensors, health monitoring, and intelligent prosthetics.

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal-oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

Suppressed Stochastic Switching Behavior and Improved Synaptic Functions in an Atomic Switch Embedded with a 2D NbSe2 Material

We investigated chemical vapor-deposited (CVD) two-dimensional (2D) niobium diselenide (NbSe₂) material for the resistive switching and synaptic characteristics. Three different atomic switch devices with Ag/HfO₂/Pt, Ag/Ti/HfO₂/Pt, and Ag/NbSe₂/HfO₂/Pt were studied as both memory and neuromorphic devices. Both the inserted Ti and NbSe₂ buffer layers effectively control the stochastic Ag-ion diffusion, leading to suppressed variation of switching characteristics, which is a critical issue in an atomic switch device. Especially, the device with the 2D NbSe₂ buffer layer strikingly enhanced the device reliability in both endurance and retention. In conjunction with scanning transmission electron microscopy (STEM) and energy-dispersive spectrometry (EDS) analysis of the control of the Ag-ion migration, it was understood that filament connection is interrelated with the SET and RESET processes. Besides resistive behaviors in the memory device, various synapse functions such as spike-rate-dependent plasticity (SRDP), forgetting curve, potentiation, and depression were demonstrated with an atomic switch with the 2D NbSe₂ buffer layer. Furthermore, the emulated long-term synaptic property was simulated using the MNIST 28 × 28 pixel database. Using adopting a CVD 2D NbSe₂ blocking layer, the stochastic Ag-ion diffusion behavior is well-controlled and therefore stable switching and synapse functions are attained.

Reliable organic memristors for neuromorphic computing by predefining a localized ion-migration path in crosslinkable polymer

In flexible neuromorphic systems for realizing artificial intelligence, organic memristors are essential building blocks as artificial synapses to perform information processing and memory. Despite much effort to implement artificial neural networks (ANNs) using organic memristors, the reliability of these devices is inherently hampered by global ion transportation and arbitrary growth of conductive filaments (CFs). As a result, the performance of ANNs is restricted. Herein, a novel concept for confining CF growth in organic memristors is demonstrated by exploiting the unique functionality of crosslinkable polymers. This can be achieved by predefining the localized ion-migration path (LIP) in crosslinkable polymers. In the proposed organic memristor, metal cations are locally transported along the LIP. Thus, CF growth is achieved only in a confined region. A flexible memristor with an LIP exhibits a vastly improved reliability and uniformity, and it is capable of operating with high mechanical and electrical endurance. Moreover, neuromorphic arrays based on the proposed memristor exhibit 96.3% learning accuracy, which is comparable to the ideal software baseline. The proposed concept of predefining the LIP in organic memristors is expected to provide novel platforms for the advance of flexible electronics and to realize a variety of practical neural networks for artificial intelligence applications.

Realization of Biomimetic Synaptic Functions in a One-Cell Organic Resistive Switching Device Using the Diffusive Parameter of Conductive Filaments

Toward the successful development of artificial intelligence, artificial synapses based on resistive switching devices are essential ingredients to perform information processing in spiking neural networks. In neural processes, synaptic plasticity related to the history of neuron activity plays a critical role during learning. In resistive switching devices, it is barely possible to emulate both short-term plasticity and long-term plasticity due to the uncontrollable dynamics of the conductive filaments (CFs). Despite extensive effort to realize synaptic plasticity in such devices, it is still challenging to achieve reliable synaptic functions due to the overgrowth of CFs in a random fashion. Herein, we propose an organic resistive switching device with bio-realistic synaptic functions by adjusting the CF diffusive parameter. In the proposed device, complete synaptic plasticity provides the history-dependent change in the conductance. Moreover, the homeostatic feedback, which resembles the biological process, regulates CF growth in our device, which enhances the reliability of synaptic plasticity. This novel concept for realizing synaptic functions in organic resistive switching devices may provide a physical platform to advance the fundamental understanding of learning and memory mechanisms and develop a variety of neural circuits and neuromorphic systems that can be linked to artificial intelligence and next-generation computing paradigm.

Toward Controlling Filament Size and Location for Resistive Switches via Nanoparticle Exsolution at Oxide Interfaces

Memristive devices are among the most prominent candidates for future computer memory storage and neuromorphic computing. Though promising, the major hurdle for their industrial fabrication is their device-to-device and cycle-to-cycle variability. These occur due to the random nature of nanoionic conductive filaments, whose rupture and formation govern device operation. Changes in filament location, shape, and chemical composition cause cycle-to-cycle variability. This challenge is tackled by spatially confining conductive filaments with Ni nanoparticles. Ni nanoparticles are integrated on the bottom La_0.2 Sr_0.7 Ti_0.9 Ni_0.1 O_{3- δ} electrode by an exsolution method, in which, at high temperatures under reducing conditions, Ni cations migrate to the perovskite surface, generating metallic nanoparticles. This fabrication method offers fine control over particle size and density and ensures strong particle anchorage in the bottom electrode, preventing movement and agglomeration. In devices based on amorphous SrTiO₃ , it is demonstrated that as the exsolved Ni nanoparticle diameter increases up to ≈50 nm, the ratio between the ON and OFF resistance states increases from single units to 180 and the variability of the low resistance state reaches values below 5%. Exsolution is applied for the first time to engineer solid-solid interfaces extending its realm of application to electronic devices.

Switching Characteristics and Mechanism Using Al2O3 Interfacial Layer in Al/Cu/GdOx/Al2O3/TiN Memristor

Resistive switching characteristics by using the Al2O3 interfacial layer in an Al/Cu/GdOx/Al2O3/TiN memristor have been enhanced as compared to the Al/Cu/GdOx/TiN structure owing to the insertion of Al2O3 layer for the first time. Polycrystalline grain, chemical composition, and surface roughness of defective GdOx film have been investigated by transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and atomic force microscopy (AFM). For bipolar resistive switching (BRS) characteristics, the conduction mechanism of high resistance state (HRS) is a space-charge limited current for the Al/Cu/GdOx/TiN device while the Al/Cu/GdOx/Al2O3/TiN device shows Schottky emission. However, both devices show Ohmic at a low resistance state (LRS). After the device has been SET, the Cu filament evidences by both TEM and elemental mapping. Oxygen-rich at the Cu/GdOx interface and Al2O3 layer are confirmed by energy dispersive X-ray spectroscopy (EDS) line profile. The Al/Cu/GdOx/Al2O3/TiN memristor has lower RESET current, higher speed operation of 100 ns, long read pulse endurance of >109 cycles, good data retention, and the memristor with a large resistance ratio of >105 is operated at a low current of 1.5 µA. The complementary resistive switching (CRS) characteristics of the Al/Cu/GdOx/Al2O3/TiN memristor show also a low current operation as compared to the Al/Cu/GdOx/TiN device (300 µA vs. 3.1 mA). The transport mechanism is the Cu ion migration and it shows Ohmic at low field and hopping at high field regions. A larger hopping distance of 1.82 nm at the Cu/GdOx interface is obtained as compared to a hopping distance of 1.14 nm in the Al2O3 layer owing to a larger Cu filament length at the Cu/GdOx interface than the Al2O3 layer. Similarly, the CRS mechanism is explained by using the schematic model. The CRS characteristics show a stable state with long endurance of >1000 cycles at a pulse width of 1 µs owing to the insertion of Al2O3 interfacial layer in the Al/Cu/GdOx/Al2O3/TiN structure.

Oxide-Electrolyte Thickness Dependence Diode-Like Threshold Switching and High on/off Ratio Characteristics by Using Al2O3 Based CBRAM

Diode-like threshold switching and high on/off ratio characteristics by using an Al/Ag/Al2O3/TiN conductive bridge resistive random access memories (CBRAM) have been obtained. The 5 nm-thick Al2O3 device shows superior memory parameters such as low forming voltage and higher switching uniformity as compared to the 20 nm-thick switching layer, owing to higher electric field across the material. Capacitance-voltage (CV) characteristics are observed for the Ag/Al2O3/TiN devices, suggesting the unipolar/bipolar resistive switching phenomena. Negative capacitance (NC) at low frequency proves inductive behavior of the CBRAM devices due to Ag ion migration into the Al2O3 oxide-electrolyte. Thicker Al2O3 film shows diode-like threshold switching behavior with long consecutive 10,000 cycles. It has been found that a thinner Al2O3 device has a larger on/off ratio of >108 as compared to a thicker one. Program/erase (P/E) cycles, read endurance, and data retention of the thinner Al2O3 oxide-electrolyte shows superior phenomena than the thicker electrolyte. The switching mechanism is also explored.

Cluster-Type Filaments Induced by Doping in Low-Operation-Current Conductive Bridge Random Access Memory

Conductive bridge random access memory (CBRAM) is one of the most representative emerging nonvolatile memories in virtue of its excellent performance on speed, high-density integration, and power efficiency. Resistive switching behaviors in CBRAM involving the formation/rupture of metallic conductive filaments are dominated by cation migration and redox processes. It is all in the pursuit to decrease the operation current for low-power consumption and to enhance the current compliance-dependent reliability. Here, we propose a novel structure of Pt/TaO_x:Ag/TaO_x/Pt with nonvolatile switching at ∼1 μA and achieve a five-resistance-state multilevel cell operation under different compliance currents. Different from the nanocone-shaped filaments reported in traditional Ag top electrode devices, cluster-type filaments were captured in our memory devices, explaining the low-operation current-resistive switching behaviors. Meanwhile, Cu-doped counterpart devices also display similar operations. Such memory devices are more inclined to achieve low-power consumption and offer feasibility to large-scale memory crossbar integration.

Challenges and Applications of Emerging Nonvolatile Memory Devices

Emerging nonvolatile memory (eNVM) devices are pushing the limits of emerging applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Among several alternatives, phase change memory, spin-transfer torque random access memory, and resistive random-access memory (RRAM) are major emerging technologies. This review explains all varieties of prototype and eNVM devices, their challenges, and their applications. A performance comparison shows that it is difficult to achieve a “universal memory” which can fulfill all requirements. Compared to other emerging alternative devices, RRAM technology is showing promise with its highly scalable, cost-effective, simple two-terminal structure, low-voltage and ultra-low-power operation capabilities, high-speed switching with high-endurance, long retention, and the possibility of three-dimensional integration for high-density applications. More precisely, this review explains the journey and device engineering of RRAM with various architectures. The challenges in different prototype and eNVM devices is disused with the conventional and novel application areas. Compare to other technologies, RRAM is the most promising approach which can be applicable as high-density memory, storage class memory, neuromorphic computing, and also in hardware security. In the post-CMOS era, a more efficient, intelligent, and secure computing system is possible to design with the help of eNVM devices.

Modulating the filament rupture degree of threshold switching device for self-selective and low-current nonvolatile memory application

Resistive switching devices have tremendous potential for memory, logic, and neuromorphic computing applications. Cation-based resistive switching devices intrinsically show nonvolatile memory characteristics under high compliance current (I _CC), while show volatile threshold switching (TS) selector characteristics under low I _CC. However, separate researches about cation-based memory or selector are hard to evade the typical current-retention dilemma, which results in the hardship to obtain low-current memory and high-current selector. Here, we propose a novel strategy to realize nonvolatile storage characteristics in a volatile TS device by modulating the rupture degree of conductive filament (CF). Enlarging the rupture degree of the CF with a certain RESET process, as confirmed by transmission electron microscope and energy dispersive spectrometry results, the threshold voltage of the Ag/HfO₂/Pt TS devices can be enlarged from 0.9 to 2.8 V. Generation of the voltage difference enables the volatile TS devices the ability of self-selective nonvolatile storage. Increasing the RESET magnitude and shrinking the device size have been proved effective ways to increase the read window of the TS memory (TSM) devices. Evading the limit of the current-retention dilemma, ultra-low energy dissipation can be obtained by decreasing I _CC to nA level. With self-selective, low-energy, and potential high-density integration characteristics, the proposed TSM device can act as a potential supplement of novel storage class memories.

Controlling Cu Migration on Resistive Switching, Artificial Synapse, and Glucose/Saliva Detection by Using an Optimized AlO x Interfacial Layer in a-CO x -Based Conductive Bridge Random Access Memory

The Cu migration is controlled by using an optimized AlO _x interfacial layer, and effects on resistive switching performance, artificial synapse, and human saliva detection in an amorphous-oxygenated-carbon (a-CO _x )-based CBRAM platform have been investigated for the first time. The 4 nm-thick AlO _x layer in the Cu/AlO _x /a-CO _x /TiN _x O _y /TiN structure shows consecutive >2000 DC switching, tight distribution of SET/RESET voltages, a long program/erase (P/E) endurance of >10⁹ cycles at a low operation current of 300 μA, and artificial synaptic characteristics under a small pulse width of 100 ns. After a P/E endurance of >10⁸ cycles, the Cu migration is observed by both ex situ high-resolution transmission electron microscopy and energy-dispersive X-ray spectroscopy mapping images. Furthermore, the optimized Cu/AlO _x /a-CO _x /TiN _x O _y /TiN CBRAM detects glucose with a low concentration of 1 pM, and real-time measurement of human saliva with a small sample volume of 1 μL is also detected repeatedly in vitro. This is owing to oxidation-reduction of Cu electrode, and the switching mechanism is explored. Therefore, this CBRAM device is beneficial for future artificial intelligence application.

All WSe2 1T1R resistive RAM cell for future monolithic 3D embedded memory integration

3D monolithic integration of logic and memory has been the most sought after solution to surpass the Von Neumann bottleneck, for which a low-temperature processed material system becomes inevitable. Two-dimensional materials, with their excellent electrical properties and low thermal budget are potential candidates. Here, we demonstrate a low-temperature hybrid co-integration of one-transistor-one-resistor memory cell, comprising a surface functionalized 2D WSe₂p-FET, with a solution-processed WSe₂ Resistive Random Access Memory. The employed plasma oxidation technique results in a low Schottky barrier height of 25 meV with a mobility of 230 cm² V⁻¹ s⁻¹, leading to a 100x performance enhanced WSe₂p-FET, while the defective WSe₂ Resistive Random Access Memory exhibits a switching energy of 2.6 pJ per bit. Furthermore, guided by our device-circuit modelling, we propose vertically stacked channel FETs for high-density sub-0.01 μm² memory cells, offering a new beyond-Si solution to enable 3-D embedded memories for future computing systems.

Designing efficient, scalable and low-thermal-budget 2D Materials for 3D integration remains a challenge. Here, the authors report the development of a hybrid-(solution-processed-exfoliated) integration of 2D Material based 1T1R which uses a multilayer WSe₂p-FET and a multilayer printed WSe₂ RRAM.

Transformation of threshold volatile switching to quantum point contact originated nonvolatile switching in graphene interface controlled memory devices

Resistive switching devices based on binary transition metal oxides have been widely investigated. However, these devices invariably manifest threshold switching characteristics when the active metal electrode is silver, the dielectric layer is hafnium oxide and platinum is used as the bottom electrode, and have a relatively low compliance current (<100 μA). Here we developed a way to transform an Ag-based hafnium oxide selector into quantum-contact originated memory with a low compliance current, in which a graphene interface barrier layer is inserted between the silver electrode and hafnium oxide layer. Devices with structure Ag/HfO x /Pt acts as a bipolar selector with a high selectivity of >108 and sub-threshold swing of ∼1 mV dec-1. After introducing a graphene interface barrier, high stress dependent (forming at +3 V) formation of localized conducting filaments embodies stable nonvolatile memory characteristics with low set/reset voltages (<±1.0 V), low reset power (6 μW) and multi-level potential. Grain boundaries of the graphene interface control the type of switching in the devices. A good barrier can switch the Ag-based volatile selector into Ag-based nonvolatile memory.

Highly Uniform Resistive Switching Performances Using Two-Dimensional Electron Gas at a Thin-Film Heterostructure for Conductive Bridge Random Access Memory

This research demonstrates, for the first time, the development of highly uniform resistive switching devices with self-compliance current for conductive bridge random access memory using two-dimensional electron gas (2DEG) at the interface of an Al₂O₃/TiO₂ thin-film heterostructure via atomic layer deposition (ALD). The cell is composed of Cu/Ti/Al₂O₃/TiO₂, where Cu/Ti and Al₂O₃ overlayers are used as the active/buffer metals and solid electrolyte, respectively, and the 2DEG at the interface of Al₂O₃/TiO₂ heterostructure, grown by the ALD process, is adopted as a bottom electrode. The Cu/Ti/Al₂O₃/TiO₂ device shows reliable resistive switching characteristics with excellent uniformity under a repetitive voltage sweep (direct current sweep). Furthermore, it exhibits a cycle endurance over 10⁷ cycles under short pulse switching. Remarkably, a reliable operation of multilevel data writing is realized up to 10⁷ cycles. The data retention time is longer than 10⁶ s at 85 °C. The uniform resistance switching characteristics are achieved via the formation of small (∼a few nm width) Cu filament with a short tunnel gap (<0.5 nm) owing to the 2DEG at the Al₂O₃/TiO₂ interface. The performance and operation scheme of this device may be appropriate in neuromorphic applications.

Interfacial Triggering of Conductive Filament Growth in Organic Flexible Memristor for High Reliability and Uniformity

We demonstrate the physical pictures of the localization of the conductive filaments (CFs) growth in flexible electrochemical metallization (ECM) memristors through an interfacial triggering (IT) into the polymer electrolyte. The IT sites (ITSs), capable of controlling the pathways of the CF growth, are formed at the electrode-polymer interfaces via the Ostwald ripening at low temperatures (below 230 °C). The injection and migration of metal ions and the resultant CF growth are found to be effectively controlled through the ITSs with the local electric field enhancement. The reliability, uniformity, and switching voltage of the device are much improved by the presence of the ITSs. Our flexible ECM memristor exhibits a high mechanical flexibility and a stable memory performance under repeated bending deformations.

Two-dimensional materials for synaptic electronics and neuromorphic systems

Synapses in biology provide a variety of functions for the neural system. Artificial synaptic electronics that mimic the biological neuron functions are basic building blocks and developing novel artificial synapses is essential for neuromorphic computation. Inspired by the unique features of biological synapses that the basic connection components of the nervous system and the parallelism, low power consumption, fault tolerance, self-learning and robustness of biological neural systems, artificial synaptic electronics and neuromorphic systems have the potential to overcome the traditional von Neumann bottleneck and create a new paradigm for dealing with complex problems such as pattern recognition, image classification, decision making and associative learning. Nowadays, two-dimensional (2D) materials have drawn great attention in simulating synaptic dynamic plasticity and neuromorphic computing with their unique properties. Here we describe the basic concepts of bio-synaptic plasticity and learning, the 2D materials library and its preparation. We review recent advances in synaptic electronics and artificial neuromorphic systems based on 2D materials and provide our perspective in utilizing 2D materials to implement synaptic electronics and neuromorphic systems in hardware.

Vacancy-Induced Synaptic Behavior in 2D WS2 Nanosheet-Based Memristor for Low-Power Neuromorphic Computing

Memristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal-ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low-power consumption requirements. Therefore, it is very necessary to develop new materials to realize memristor devices that are different from the mechanisms of oxygen vacancy or metal-ion conductive filaments to realize low-power operation. Herein, high-performance and low-power consumption memristors based on 2D WS₂ with 2H phase are demonstrated, which show fast ON (OFF) switching times of 13 ns (14 ns), low program current of 1 µA in the ON state, and SET (RESET) energy reaching the level of femtojoules. Moreover, the memristor can mimic basic biological synaptic functions. Importantly, it is proposed that the generation of sulfur and tungsten vacancies and electron hopping between vacancies are dominantly responsible for the resistance switching performance. Density functional theory calculations show that the defect states formed by sulfur and tungsten vacancies are at deep levels, which prevent charge leakage and facilitate the realization of low-power consumption for neuromorphic computing application.

Mediation in the second-order synaptic emulator with conductive atomic force microscopy

Memristors have been extensively studied for synaptic simulation and neuromorphic computation. Instead of focusing on implementing specific synaptic learning rules by carefully engineering external programming parameters, researchers recently have paid more attention to taking advantage of the second-order memristor that is more analogous to biologic synapses and modulated not only by external inputs but also by internal mechanisms. However, experimental evidence is still scarce. Here, we explore a BiMnO3 memristor by applying simple spike forms. The filament evolution dynamics, including processes of forming and spontaneous decay, were directly observed by the conductive atomic force microscopy (c-AFM) technique. We propose that the unique conductance state of the BMO memristor is regulated by the oxygen vacancy (VO) dynamic process. We believe this primary result is helpful to improve understanding of the internal mechanisms of the second-order oxide memristor, which exhibits promising application in building selectors, memories and neuromorphic-computing systems.

Suppression of Filament Overgrowth in Conductive Bridge Random Access Memory by Ta2O5/TaOx Bi-Layer Structure

Bi-layer structure has been widely adopted to improve the reliability of the conductive bridge random access memory (CBRAM). In this work, we proposed a convenient and economical solution to achieve a Ta2O5/TaOx bi-layer structure by using a low-temperature annealing process. The addition of a TaOx layer acted as an external resistance suppressing the overflow current during set programming, thus achieving the self-compliance switching. As a result, the distributions of high-resistance states and low-resistance states are improved due to the suppression of the overset phenomenon. In addition, the LRS retention of the CBRAM is obviously enhanced due to the recovery of defects in the switching film. This work provides a simple and economical method to improve the reliability of CBRAM.

Effect of dysprosium and lutetium metal buffer layers on the resistive switching characteristics of Cu-Sn alloy-based conductive-bridge random access memory

The conductive-bridge random access memory (CBRAM) has become one of the most suitable candidates for non-volatile memory in next-generation information and communication technology. The resistive switching (RS) mechanism of CBRAM depends on the formation/annihilation of the conductive filament (CF) between the active metal electrode and the inert electrode. However, excessive ion injection from the active electrode into the solid electrolyte reduces the uniformity and reliability of the RS devices. To solve this problem, we investigated the RS characteristics of a CuSn alloy active electrode with different compositions of Cu_x-Sn_1-x (0.13 < X < 0.55). The RS characteristics were further improved by inserting a dysprosium (Dy) or lutetium (Lu) buffer layer at the interface of Cu_x-Sn_1-x/Al₂O₃. Electrical analysis of the optimal Cu_0.4-Sn_0.73/Lu-based CBRAM exhibited stable RS behavior with low operation voltage (SET: 0.7 V and RESET: -0.3 V), a high on state/off state resistive ratio (10⁶), AC cyclic endurance (>10⁴), and stable retention (85 °C/10 years). To achieve these performance parameters, CFs were locally formed inside the electrolyte using a modified CuSn active electrode, and the amount of Cu-ion injection was reduced by inserting the Dy or Lu buffer layer between the CuSn active electrode and the electrolyte. In particular, conductive-atomic force microscopy results at the Dy or Lu/Al₂O₃ interface directly showed and defined the diameter of the CF.

Breaking the Current-Retention Dilemma in Cation-Based Resistive Switching Devices Utilizing Graphene with Controlled Defects

Cation-based resistive switching (RS) devices, dominated by conductive filaments (CF) formation/dissolution, are widely considered for the ultrahigh density nonvolatile memory application. However, the current-retention dilemma that the CF stability deteriorates greatly with decreasing compliance current makes it hard to decrease operating current for memory application and increase driving current for selector application. By centralizing/decentralizing the CF distribution, this current-retention dilemma of cation-based RS devices is broken for the first time. Utilizing the graphene impermeability, the cation injecting path to the RS layer can be well modulated by structure-defective graphene, leading to control of the CF quantity and size. By graphene defect engineering, a low operating current (≈1 µA) memory and a high driving current (≈1 mA) selector are successfully realized in the same material system. Based on systematically materials analysis, the diameter of CF, modulated by graphene defect size, is the major factor for CF stability. Breakthrough in addressing the current-retention dilemma will instruct the future implementation of high-density 3D integration of RS memory immune to crosstalk issues.

Negative differential resistance effect induced by metal ion implantation in SiO2 film for multilevel RRAM application

Pt/SiO₂:metal nanoparticles/Pt sandwich structure is fabricated with the method of metal ion (Ag) implantation. The device exhibits multilevel storage with appropriate R _off/R _on ratio, good endurance and retention properties. Based on transmission electron microscopy and energy dispersive spectrometer analysis, we confirm that Pt nanoparticles are spurted into SiO₂ film from Pt bottom electrode by Ag implantation; during electroforming, the local electric field can be enhanced by these Pt nanoparticles, meanwhile the Ag nanoparticles constantly migrate toward the Pt nanoparticles. The implantation induced nanoparticles act as trap sites in the resistive switching layer and play critical roles in the multilevel storage, which is evidenced by the negative differential resistance effect in the current-voltage (I-V) measurements.

Threshold Switching Induced by Controllable Fragmentation in Silver Nanowire Networks

Silver nanowire (Ag NW) networks have been widely studied because of a great potential in various electronic devices. However, nanowires usually undergo a fragmentation process at elevated temperatures due to the Rayleigh instability that is a result of reduction of surface/interface energy. In this case, the nanowires become completely insulating due to the formation of randomly distributed Ag particles with a large distance and further applications are hindered. Herein, we demonstrate a novel concept based on the combination of ultraviolet/ozone irradiation and a low-temperature annealing process to effectively utilize and control the fragmentation behavior to realize the resistive switching performances. In contrast to the conventional fragmentation, the designed Ag/AgO_x interface facilitates a unique morphology of short nanorod-like segments or chains of tiny Ag nanoparticles with a very small spacing distance, providing conduction paths for achieving the tunneling process between the isolated fragments under the electric field. On the basis of this specific morphology, the Ag NW network has a tunable resistance and shows volatile threshold switching characteristics with a high selectivity, which is the ON/OFF current ratio in selector devices. Our concept exploits a new function of Ag NW network, i.e., resistive switching, which can be developed by designing a controllable fragmentation.

On-Demand Reconfiguration of Nanomaterials: When Electronics Meets Ionics

Rapid advances in the semiconductor industry, driven largely by device scaling, are now approaching fundamental physical limits and face severe power, performance, and cost constraints. Multifunctional materials and devices may lead to a paradigm shift toward new, intelligent, and efficient computing systems, and are being extensively studied. Herein examines how, by controlling the internal ion distribution in a solid-state film, a material's chemical composition and physical properties can be reversibly reconfigured using an applied electric field, at room temperature and after device fabrication. Reconfigurability is observed in a wide range of materials, including commonly used dielectric films, and has led to the development of new device concepts such as resistive random-access memory. Physical reconfigurability further allows memory and logic operations to be merged in the same device for efficient in-memory computing and neuromorphic computing systems. By directly changing the chemical composition of the material, coupled electrical, optical, and magnetic effects can also be obtained. A survey of recent fundamental material and device studies that reveal the dynamic ionic processes is included, along with discussions on systematic modeling efforts, device and material challenges, and future research directions.

Reliable Multistate Data Storage with Low Power Consumption by Selective Oxidation of Pyramid-Structured Resistive Memory

Multilevel data storage using resistive random access memory (RRAM) has attracted significant attention for addressing the challenges associated with the rapid advances in information technologies. However, it is still difficult to secure reliable multilevel resistive switching of RRAM due to the stochastic and multiple formation of conductive filaments (CFs). Herein, we demonstrate that a single CF, derived from selective oxidation by a structured Cu active electrode, can solve the reliability issue. High-quality pyramidal Cu electrodes with a sharp tip are prepared via the template-stripping method. Morphology-dependent surface energy facilitates the oxidation of Cu atoms at the tip rather than in other regions, and the tip-enhanced electric fields can accelerate the transport of the generated Cu ions. As a result, CF growth occurs mainly at the tip of the pyramidal electrode, which is confirmed by high-resolution electron microscopy and elemental analysis. The RRAM exhibits highly uniform and low forming voltages (the average forming voltage and its standard deviation for 20 pyramid-based RRAMs are 0.645 and 0.072 V, respectively). Moreover, all multilevel resistance states for the RRAMs are clearly distinguished and show narrow distributions within 1 order of magnitude, leading to reliable cell-to-cell performance for MLC operation.

Digital to analog resistive switching transition induced by graphene buffer layer in strontium titanate based devices

Resistive switching behaviour can be classified into digital and analog switching based on its abrupt and gradual resistance change characteristics. Realizing the transition from digital to analog switching in the same device is essential for understanding and controlling the performance of the devices with various switching mechanisms. Here, we investigate the resistive switching in a device made with strontium titanate (SrTiO₃) nanoparticles using X-ray diffractometry, scanning electron microscopy, Raman spectroscopy, and direct electrical measurements. It is found that the well-known rupture/formation of Ag filaments is responsible for the digital switching in the device with Ag as the top electrode. To modulate the switching performance, we insert a reduced graphene oxide layer between SrTiO₃ and the bottom FTO electrode owing to its good barrier property for the diffusion of Ag ions and high out-of-plane resistance. In this case, resistive switching is changed from digital to analog as determined by the modulation of interfacial resistance under applied voltage. Based on that controllable resistance, potentiation and depression behaviours are implemented as well. This study opens up new ways for the design of multifunctional devices which are promising for memory and neuromorphic computing applications.

Customized binary and multi-level HfO2-x-based memristors tuned by oxidation conditions

The memristor is a promising candidate for the next generation non-volatile memory, especially based on HfO2-x, given its compatibility with advanced CMOS technologies. Although various resistive transitions were reported independently, customized binary and multi-level memristors in unified HfO2-x material have not been studied. Here we report Pt/HfO2-x/Ti memristors with double memristive modes, forming-free and low operation voltage, which were tuned by oxidation conditions of HfO2-x films. As O/Hf ratios of HfO2-x films increase, the forming voltages, SET voltages, and Roff/Ron windows increase regularly while their resistive transitions undergo from gradually to sharply in I/V sweep. Two memristors with typical resistive transitions were studied to customize binary and multi-level memristive modes, respectively. For binary mode, high-speed switching with 103 pulses (10 ns) and retention test at 85 °C (>104 s) were achieved. For multi-level mode, the 12-levels stable resistance states were confirmed by ongoing multi-window switching (ranging from 10 ns to 1 μs and completing 10 cycles of each pulse). Our customized binary and multi-level HfO2-x-based memristors show high-speed switching, multi-level storage and excellent stability, which can be separately applied to logic computing and neuromorphic computing, further suitable for in-memory computing chip when deposition atmosphere may be fine-tuned.

Real-Time Observation of the Electrode-Size-Dependent Evolution Dynamics of the Conducting Filaments in a SiO2 Layer

Conducting bridge random access memory (CBRAM) is one of the most promising candidates for future nonvolatile memories. It is important to understand the scalability and retention of CBRAM cells to realize better memory performance. Here, we directly observe the switching dynamics of Cu tip/SiO₂/W cells with various active electrode sizes using in situ transmission electron microscopy. Conducting filaments (CFs) grow from the active electrode (Cu tip) to inert electrode (W) during the SET operations. The size of the Cu tip affects the electric-field distribution, the amount of the cation injection into electrolyte, and the dimension of the CF. This study provides helpful understanding on the relationship between power consumption and retention of CBRAM cells. We also construct a theoretical model to explain the electrode-size-dependent CF growth in SET operations, showing good agreement with our experimental results.