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

High-Linearity Ta2O5 Memristor and Its Application in Gaussian Convolution Image Denoising

In the image Gaussian filtering process, convolving with a Gaussian matrix is essential due to the numerous arithmetic computations involved, predominantly multiplications and additions. This can heavily tax the system's memory, particularly with frequent use. To address this issue, a W/Ta2O5/Ag memristor was employed to substantially mitigate the computational overhead associated with convolution operations. Additionally, an interlayer of ZnO was subsequently introduced into the memristor. The resulting Ta2O5/ZnO heterostructure layer exhibited improved linearity in the pulse response, which enhanced linearity facilitates easy adjustment of the conductance magnitude through a linear mapping of the number of pulses and the conductance. Subsequently, the conductance of the W/Ta2O5/ZnO/Ag bilayer memristor was employed as the weights for the convolution kernel in convolution operations. Gaussian noise removal in image processing was achieved by assembling a 5 × 5 memristor array as the kernel. When denoising was performed using memristor arrays, compared to denoising achieved through Gaussian matrix convolution, an average loss of less than 5% was observed. The provided memristors demonstrate significant potential in convolutional computations, particularly for subsequent applications in convolutional neural networks (CNNs).

Thermally Oxidized Memristor and 1T1R Integration for Selector Function and Low-Power Memory

Resistive switching memories have garnered significant attention due to their high-density integration and rapid in-memory computing beyond von Neumann's architecture. However, significant challenges are posed in practical applications with respect to their manufacturing process complexity, a leakage current of high resistance state (HRS), and the sneak-path current problem that limits their scalability. Here, a mild-temperature thermal oxidation technique for the fabrication of low-power and ultra-steep memristor based on Ag/TiOx/SnOx/SnSe2/Au architecture is developed. Benefiting from a self-assembled oxidation layer and the formation/rupture of oxygen vacancy conductive filaments, the device exhibits an exceptional threshold switching behavior with high switch ratio exceeding 106, low threshold voltage of ≈1 V, long-term retention of >104 s, an ultra-small subthreshold swing of 2.5 mV decade-1 and high air-stability surpassing 4 months. By decreasing temperature, the device undergoes a transition from unipolar volatile to bipolar nonvolatile characteristics, elucidating the role of oxygen vacancies migration on the resistive switching process. Further, the 1T1R structure is established between a memristor and a 2H-MoTe2 transistor by the van der Waals (vdW) stacking approach, achieving the functionality of selector and multi-value memory with lower power consumption. This work provides a mild-thermal oxidation technology for the low-cost production of high-performance memristors toward future in-memory computing applications.

Sodium-Controlled Interfacial Resistive Switching in Thin Film Niobium Oxide for Neuromorphic Applications

A double layer 2-terminal device is employed to show Na-controlled interfacial resistive switching and neuromorphic behavior. The bilayer is based on interfacing biocompatible NaNbO3 and Nb2O5, which allows the reversible uptake of Na+ in the Nb2O5 layer. We demonstrate voltage-controlled interfacial barrier tuning via Na+ transfer, enabling conductivity modulation and spike-amplitude- and spike-timing-dependent plasticity. The neuromorphic behavior controlled by Na+ ion dynamics in biocompatible materials shows potential for future low-power sensing electronics and smart wearables with local processing.

Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

Study on the Sodium-Doped Titania Interface-Type Memristor

Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for analog in-memory computing accelerators. However, the relatively low reliability of the memristor device and sneak current issues in CAA remain the main obstacles. Alkali ion-based interface-type memristors are promising solutions for implementing highly reliable memristor devices and neuromorphic hardware. This interface-type device benefits from self-rectifying and forming-free resistive switching (RS), and exhibits relatively low variation from device to device and cycle to cycle. In a previous report, we introduced an in situ grown Na/TiO2 memristor using atomic layer deposition (ALD) and proposed a RS mechanism from experimentally measured Schottky barrier modulation. Self-rectifying RS characteristics were observed by the asymmetric distribution of Na dopants and oxygen vacancies as the Ti metal used as the adhesion layer for the bottom electrode diffuses over the Pt electrode at 250 °C during the ALD process and is doped into the TiO2 layer. Here, we theoretically verify the modulation of the Schottky barrier at the TiO2/Pt electrode interface by Na ions. This study fabricated a Pt/Na/TiO2/Pt memristor device and confirmed its self-rectifying RS characteristics and stable retention characteristics for 24 h at 85 °C. Additionally, this device exhibited relative standard deviations of 27 and 7% in the high and low resistance states, respectively, in terms of cycle-to-cycle variation. To verify the RS mechanism, we conducted density functional theory simulations to analyze the impact of Na cations at interstitial sites on the Schottky barrier. Our findings can contribute to both fundamental understanding and the design of high-performance memristor devices for neuromorphic computing.

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.

High-Reliability and Self-Rectifying Alkali Ion Memristor through Bottom Electrode Design and Dopant Incorporation

Ionic memristor devices are crucial for efficient artificial neural network computations in neuromorphic hardware. They excel in multi-bit implementation but face challenges like device reliability and sneak currents in crossbar array architecture (CAA). Interface-type ionic memristors offer low variation, self-rectification, and no forming process, making them suitable for CAA. However, they suffer from slow weight updates and poor retention and endurance. To address these issues, the study demonstrated an alkali ion self-rectifying memristor with an alkali metal reservoir formed by a bottom electrode design. By adopting Li metal as the adhesion layer of the bottom electrode, an alkali ion reservoir was formed at the bottom of the memristor layer by diffusion occurring during the atomic layer deposition process for the Na:TiO2 memristor layer. In addition, Al dopant was used to improve the retention characteristics by suppressing the diffusion of alkali cations. In the memristor device with optimized Al doping, retention characteristics of more than 20 h at 125 °C, endurance characteristics of more than 5.5 × 105, and high linearity/symmetry of weight update characteristics were achieved. In reliability tests on 100 randomly selected devices from a 32 × 32 CAA device, device-to-device and cycle-to-cycle variations showed low variation values within 81% and 8%, respectively.

Novel Three-Dimensional Artificial Neural Network Based on an Eight-Layer Vertical Memristor with an Ultrahigh Rectify Ratio (>107) and an Ultrahigh Nonlinearity (>105) for Neuromorphic Computing

In recent years, memristors have successfully demonstrated their significant potential in artificial neural networks (ANNs) and neuromorphic computing. Nonetheless, ANNs constructed by crossbar arrays suffer from cross-talk issues and low integration densities. Here, we propose an eight-layer three-dimensional (3D) vertical crossbar memristor with an ultrahigh rectify ratio (RR > 107) and an ultrahigh nonlinearity (>105) to overcome these limitations, which enables it to reach a >1 Tb array size without reading failure. Furthermore, the proposed 3D RRAM shows advanced endurance (>1010 cycles), retention (>104 s), and uniformity. In addition, several synaptic functions observed in the human brain were mimicked. On the basis of the advanced performance, we constructed a novel 3D ANN, whose learning efficiency and recognition accuracy were enhanced significantly compared with those of conventional single-layer ANNs. These findings hold promise for the development of highly efficient, precise, integrated, and stable VLSI neuromorphic computing systems.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

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.

Tunable Resistive Switching Behaviors and Mechanism of the W/ZnO/ITO Memory Cell

A facile sol-gel spin coating method has been proposed for the synthesis of spin-coated ZnO nanofilms on ITO substrates. The as-prepared ZnO-nanofilm-based W/ZnO/ITO memory cell showed forming-free and tunable nonvolatile multilevel resistive switching behaviors with a high resistance ratio of about two orders of magnitude, which can be maintained for over 10³ s and without evident deterioration. The tunable nonvolatile multilevel resistive switching phenomena were achieved by modulating the different set voltages of the W/ZnO/ITO memory cell. In addition, the tunable nonvolatile resistive switching behaviors of the ZnO-nanofilm-based W/ZnO/ITO memory cell can be interpreted by the partial formation and rupture of conductive nanofilaments modified by the oxygen vacancies. This work demonstrates that the ZnO-nanofilm-based W/ZnO/ITO memory cell may be a potential candidate for future high-density, nonvolatile, memory applications.

Double-Floating-Gate van der Waals Transistor for High-Precision Synaptic Operations

Two-dimensional materials and their heterostructures have thus far been identified as leading candidates for nanoelectronics owing to the near-atom thickness, superior electrostatic control, and adjustable device architecture. These characteristics are indeed advantageous for neuro-inspired computing hardware where precise programming is strongly required. However, its successful demonstration fully utilizing all of the given benefits remains to be further developed. Herein, we present van der Waals (vdW) integrated synaptic transistors with multistacked floating gates, which are reconfigured upon surface oxidation. When compared with a conventional device structure with a single floating gate, our double-floating-gate (DFG) device exhibits better nonvolatile memory performance, including a large memory window (>100 V), high on-off current ratio (∼10⁷), relatively long retention time (>5000 s), and satisfactory cyclic endurance (>500 cycles), all of which can be attributed to its increased charge-storage capacity and spatial redistribution. This facilitates highly effective modulation of trapped charge density with a large dynamic range. Consequently, the DFG transistor exhibits an improved weight update profile in long-term potentiation/depression synaptic behavior for nearly ideal classification accuracies of up to 96.12% (MNIST) and 81.68% (Fashion-MNIST). Our work adds a powerful option to vdW-bonded device structures for highly efficient neuromorphic computing.

The Effect of Nitrogen Annealing on the Resistive Switching Characteristics of the W/TiO2/FTO Memory Device

In this paper, the effect of nitrogen annealing on the resistive switching characteristics of the rutile TiO₂ nanowire-based W/TiO₂/FTO memory device is analyzed. The W/TiO₂/FTO memory device exhibits a nonvolatile bipolar resistive switching behavior with a high resistance ratio (R_HRS/R_LRS) of about two orders of magnitude. The conduction behaviors of the W/TiO₂/FTO memory device are attributed to the Ohmic conduction mechanism and the Schottky emission in the low resistance state and the high resistance state, respectively. Furthermore, the R_HRS/R_LRS of the W/TiO₂/FTO memory device is obviously increased from about two orders of magnitude to three orders of magnitude after the rapid nitrogen annealing treatment. In addition, the change in the W/TiO₂ Schottky barrier depletion layer thickness and barrier height modified by the oxygen vacancies at the W/TiO₂ interface is suggested to be responsible for the resistive switching characteristics of the W/TiO₂/FTO memory device. This work demonstrates the potential applications of the rutile TiO₂ nanowire-based W/TiO₂/FTO memory device for high-density data storage in nonvolatile memory 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.

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.

Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware

To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-to-cycle and device-to-device variations. This study experimentally demonstrated and compared the filamentary and interface-type resistive switching (RS) behaviors of tantalum oxide (Ta₂O₅ and TaO₂)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta₂O₅ is a strong candidate for memristor, the filament-type RS behavior of Ta₂O₅ does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-type TaO₂ memristor and compared it to a filament type of Ta₂O₅ memristor to secure the weight update characteristics and reliability. The TaO₂-based interface-type memristor exhibited gradual RS characteristics and area dependency in both high- and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO₂-based interface-type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-term potentiation (LTP) and long-term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.

All-Printed Flexible Memristor with Metal–Non-Metal-Doped TiO2 Nanoparticle Thin Films

A memristor is a fundamental electronic device that operates like a biological synapse and is considered as the solution of classical von Neumann computers. Here, a fully printed and flexible memristor is fabricated by depositing a thin film of metal–non-metal (chromium-nitrogen)-doped titanium dioxide (TiO₂). The resulting device exhibited enhanced performance with self-rectifying and forming free bipolar switching behavior. Doping was performed to bring stability in the performance of the memristor by controlling the defects and impurity levels. The forming free memristor exhibited characteristic behavior of bipolar resistive switching with a high on/off ratio (2.5 × 10³), high endurance (500 cycles), long retention time (5 × 10³ s) and low operating voltage (±1 V). Doping the thin film of TiO₂ with metal–non-metal had a significant effect on the switching properties and conduction mechanism as it directly affected the energy bandgap by lowering it from 3.2 eV to 2.76 eV. Doping enhanced the mobility of charge carriers and eased the process of filament formation by suppressing its randomness between electrodes under the applied electric field. Furthermore, metal–non-metal-doped TiO₂ thin film exhibited less switching current and improved non-linearity by controlling the surface defects.