A Modified SiO2-Based Memristor with Reliable Switching and Multifunctional Synaptic Behaviors

Reconfigurable Al2O3-Based Memristor for All-in-One Artificial Synapse and Nociceptor Neurons

Multifunctional bionic devices have widespread applications in neuromorphic computing, intelligent sensors, and robotics. The inherent properties of memristors make them suitable for these emerging applications, but different applications require either volatile or nonvolatile operations in a unique device. In this work, we have developed a novel reconfigurable Ag/Al2O3/ITO memristor, which achieves adjustable switching behavior between volatile switching and nonvolatile switching by modulating the compliance current. A proposed mechanism controls the state of the conductive filaments in the device by adjusting compliance current, elucidating the adjustable switching process between volatile and nonvolatile states. Additionally, the synaptic functionality and nociceptor characteristics, including threshold, relaxation, inadaptation, and sensitization, have been successfully simulated. This integration of artificial synaptic and nociceptor functions into a single device is achieved, with the single-pulse power consumption of the nociceptor reaching as low as 0.912 nJ when the threshold is reached. These results provide insights into the construction of multifunctional bionic devices and demonstrate significant potential for future neuromorphic network applications.

Memristive Ion Dynamics to Enable Biorealistic Computing

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention. Ion-based memristive devices (IMDs), owing to the intrinsic functional similarities to their biological counterparts, hold significant promise for implementing emerging neuromorphic learning and computing algorithms. In this article, we review the fundamental mechanisms of IMDs based on ion drift and diffusion to elucidate the origins of their diverse dynamics. We then examine how these mechanisms operate within different materials to enable IMDs with various types of switching behaviors, leading to a wide range of applications, from emulating biological components to realizing specialized computing requirements. Furthermore, we explore the potential for IMDs to be modified and tuned to achieve customized dynamics, which positions them as one of the most promising hardware candidates for executing bioinspired algorithms with unique specifications. Finally, we identify the challenges currently facing IMDs that hinder their widespread usage and highlight emerging research directions that could significantly benefit from incorporating IMDs.

Kinetics of Volatile and Nonvolatile Halide Perovskite Devices: The Conductance-Activated Quasi-Linear Memristor (CALM) Model

Memristors stand out as promising components in the landscape of memory and computing. Memristors are generally defined by a conductance mechanism containing a state variable that imparts a memory effect. The current-voltage cycling causes transitions of conductance, which are determined by different physical mechanisms, such as the formation of conducting filaments in an insulating surrounding. Here, we provide a unified description of the set and reset processes using a conductance-activated quasi-linear memristor (CALM) model with a unique voltage-dependent relaxation time of the memory variable. We focus on halide perovskite memristors and their intersection with neuroscience-inspired computing. We show that the modeling approach adeptly replicates the experimental traits of both volatile and nonvolatile memristors. Its versatility extends across various device materials and configurations, as W/SiGe/a-Si/Ag, Si/SiO2/Ag, and SrRuO3/Cr-SrZrO3/Au memristors, capturing nuanced behaviors such as scan rate and upper vertex dependence. The model also describes the response to sequences of voltage pulses that cause synaptic potentiation effects. This model is a potent tool for comprehending and probing the dynamical response of memristors by indicating the relaxation properties that control observable responses.

Leveraging Tunability of Localized-Interfacial Memristors for Efficient Handling of Complex Neural Networks

A scalable (<130 nm) resistive switching memristor that features both filamentary and interfacial switching aimed at neuromorphic computing is developed in this study. The typically perceived noise or volatility was effectively harnessed as a controlled mechanism for interfacial switching. The multilayer structure for the proposed memristor enhances switching stability by curbing ionic overmigration and mitigating leakage paths. Furthermore, the memristors showcased their reliability by demonstrating more than 15 M cycles in the filamentary mode and 1 M pulses in the interfacial mode. Additionally, retention tests at 85 °C for 104 s confirmed the stability across different states, affirming its reliability as a nonvolatile CMOS-compatible element. While many studies validate performance solely on the MNIST data set, this work also evaluates more complex data sets, demonstrating the robustness of the demonstrated memristor in supervised learning. Specifically, supervised learning simulations on MNIST and fashion MNIST data sets indicated a high learning rate with <4% deviations from numerical training, while offline inference trained on CIFAR-10 and CIFAR-100 data sets revealed <2.5% and <7% deviations caused by programing error accumulation, even with increased memristor counts for these highly complex data sets. Unsupervised learning via spike-timing-dependent plasticity further highlights the potential of the developed memristor in bridging artificial and biological paradigms, offering a significant advance toward efficient and biologically inspired computing architectures.

MoOx Synaptic Memristor with Programmable Multilevel Conductance for Reliable Neuromorphic Hardware

Memristor holds great potential for enabling next-generation neuromorphic computing hardware. Controlling the interfacial characteristics of the device is critical for seamlessly integrating and replicating the synaptic dynamic behaviors; however, it is commonly overlooked. Herein, we report the straightforward oxidation of a Mo electrode in air to design MoOx memristors that exhibit nonvolatile ultrafast switching (0.6-0.8 mV/decade, <1 mV/decade) with a high on/off ratio (>104), a long durability (>104 s), a low power consumption (17.9 μW), excellent device-to-device uniformity, ingeniously synaptic behavior, and finely programmable multilevel analog switching. The analyzed physical mechanism of the observed resistive switching behavior might be the conductive filaments formed by the oxygen vacancies. Intriguingly, upon organization into memristor-based crossbar arrays, in addition to simulated multipattern memorization, edge detection on random images can be implemented well by parallel processing of pixels using a 3 × 3 × 2 array of Prewitt filter groups. These are vital functions for neural system hardware in efficient in-memory computing neural systems with massive parallelism beyond a von Neumann architecture.

Self-Assembled Au Nanoelectrodes: Enabling Low-Threshold-Voltage HfO2-Based Artificial Neurons

Filamentary-type resistive switching devices, such as conductive bridge random-access memory and valence change memory, have diverse applications in memory and neuromorphic computing. However, the randomness in filament formation poses challenges to device reliability and uniformity. To overcome this issue, various defect engineering methods have been explored, including doping, metal nanoparticle embedding, and extended defect utilization. In this study, we present a simple and effective approach using self-assembled uniform Au nanoelectrodes to controll filament formation in HfO2 resistive switching devices. By concentrating the electric field near the Au nanoelectrodes within the BaTiO3 matrix, we significantly enhanced the device stability and reduced the threshold voltage by up to 45% in HfO2-based artificial neurons compared to the control devices. The threshold voltage reduction is attributed to the uniformly distributed Au nanoelectrodes in the insulating matrix, as confirmed by COMSOL simulation. Our findings highlight the potential of nanostructure design for precise control of filamentary-type resistive switching devices.

Optoelectronic Memristive Synapse Behavior for the Architecture of Cu2ZnSnS4@BiOBr Embedded in Poly(methyl methacrylate)

The great potential of artificial optoelectronic devices that are capable of mimicking biosynapse functions in brain-like neuromorphic computing applications has aroused extensive interest, and the architecture design is decisive yet challenging. Herein, a new architecture of p-type Cu₂ZnSnS₄@BiOBr nanosheets embedded in poly(methyl methacrylate) (PMMA) films (CZTS@BOB-PMMA) is presented acting as a switching layer, which not only shows the bipolar resistive switching features (SET/RESET voltages, ∼ -0.93/+1.35 V; retention, >10⁴ s) and electrical- and near-infrared light-induced synapse plasticity but also demonstrates electrical-driven excitatory postsynaptic current, spiking-time-dependent plasticity, paired pulse facilitation, long-term plasticity, long- and short-term memory, and "learning-forgetting-learning" behaviors. The approach is a rewarding attempt to broaden the research of optoelectric controllable memristive devices for building neuromorphic architectures mimicking human brain functionalities.

Mechanistic and Kinetic Analysis of Perovskite Memristors with Buffer Layers: The Case of a Two-Step Set Process

With the increasing demand for artificially intelligent hardware systems for brain-inspired in-memory and neuromorphic computing, understanding the underlying mechanisms in the resistive switching of memristor devices is of paramount importance. Here, we demonstrate a two-step resistive switching set process involving a complex interplay among mobile halide ions/vacancies (I^-/V_I⁺) and silver ions (Ag⁺) in perovskite-based memristors with thin undoped buffer layers. The resistive switching involves an initial gradual increase in current associated with a drift-related halide migration within the perovskite bulk layer followed by an abrupt resistive switching associated with diffusion of mobile Ag⁺ conductive filamentary formation. Furthermore, we develop a dynamical model that explains the characteristic I-V curve that helps to untangle and quantify the switching regimes consistent with the experimental memristive response. This further insight into the two-step set process provides another degree of freedom in device design for versatile applications with varying levels of complexity.

Multiphotoconductance Levels of the Organic Semiconductor of Polyimide-Based Memristor Induced by Interface Charges

A memristor with Au/polyimide (PI)/Au structure is prepared by magnetron sputtering to investigate the multiphotoconductance resistive switching (RS) memory behavior. The PI-based memristor presents stable bipolar RS memory and is sensitive to visible light. Four discrete conductance states in both high-resistance state (HRS) and low-resistance state (LRS) are obtained when illuminating by 365, 550, 590, and 780 nm light. Electron trapping and detrapping from the defects distributed at interfaces and the PI switching layer are responsible for the observed RS memory behavior. The enhanced trapping and detrapping process by light illumination is responsible for the multiconductance states. This work provides the possibility for further development of neuromorphic vision sensors using an organic semiconductor-based memristor.

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.