Nonvolatile Memory Materials for Neuromorphic Intelligent Machines

Threshold learning algorithm for memristive neural network with binary switching behavior

On-chip learning is an effective method for adjusting artificial neural networks in neuromorphic computing systems by considering hardware intrinsic properties. However, it faces challenges due to hardware nonidealities, such as the nonlinearity of potentiation and depression and limitations on fine weight adjustment. In this study, we propose a threshold learning algorithm for a variation-tolerant ternary neural network in a memristor crossbar array. This algorithm utilizes two tightly separated resistance states in memristive devices to represent weight values. The high-resistance state (HRS) and low-resistance state (LRS) defined as read current of < 0.1 μA and > 1 μA, respectively, were successfully programmed in a 32 × 32 crossbar array, and exhibited half-normal distributions due to the programming method. To validate our approach experimentally, a 64 × 10 single-layer fully connected network were trained in the fabricated crossbar for an 8 × 8 MNIST dataset using the threshold learning algorithm, where the weight value is updated when a gradient determined by backpropagation exceeds a threshold value. Thanks to the large margin between the two states of the memristor, we observed only a 0.42 % drop in classification accuracy compared to the baseline network results. The threshold learning algorithm is expected to alleviate the programming burden and be utilized in variation-tolerant neuromorphic architectures.

Memristive Crossbar Array-Based Probabilistic Graph Modeling

Modern graph datasets with structural complexity and uncertainties due to incomplete information or data variability require advanced modeling techniques beyond conventional graph models. This study introduces a memristive crossbar array (CBA)-based probabilistic graph model (C-PGM) utilizing Cu0.3Te0.7/HfO2/Pt memristors, which exhibit probabilistic switching, self-rectifying, and memory characteristics. C-PGM addresses the complexities and uncertainties inherent in structural graph data across various domains, leveraging the probabilistic nature of memristors. C-PGM relies on the device-to-device variation across multiple memristive CBAs, overcoming the limitations of previous approaches that rely on sequential operations, which are slower and have a reliability concern due to repeated switching. This new approach enables the fast processing and massive implementation of probabilistic units at the expense of chip area. In this study, the hardware-based C-PGM feasibly expresses small-scale probabilistic graphs and shows minimal error in aggregate probability calculations. The probability calculation capabilities of C-PGM are applied to steady-state estimation and the PageRank algorithm, which is implemented on a simulated large-scale C-PGM. The C-PGM-based steady-state estimation and PageRank algorithm demonstrate comparable accuracy to conventional methods while significantly reducing computational costs.

Review of neuromorphic computing based on NAND flash memory

The proliferation of data has facilitated global accessibility, which demands escalating amounts of power for data storage and processing purposes. In recent years, there has been a rise in research in the field of neuromorphic electronics, which draws inspiration from biological neurons and synapses. These electronics possess the ability to perform in-memory computing, which helps alleviate the limitations imposed by the 'von Neumann bottleneck' that exists between the memory and processor in the traditional von Neumann architecture. By leveraging their multi-bit non-volatility, characteristics that mimic biology, and Kirchhoff's law, neuromorphic electronics offer a promising solution to reduce the power consumption in processing vector-matrix multiplication tasks. Among all the existing nonvolatile memory technologies, NAND flash memory is one of the most competitive integrated solutions for the storage of large volumes of data. This work provides a comprehensive overview of the recent developments in neuromorphic computing based on NAND flash memory. Neuromorphic architectures using NAND flash memory for off-chip learning are presented with various quantization levels of input and weight. Next, neuromorphic architectures for on-chip learning are presented using standard backpropagation and feedback alignment algorithms. The array architecture, operation scheme, and electrical characteristics of NAND flash memory are discussed with a focus on the use of NAND flash memory in various neural network structures. Furthermore, the discrepancy of array architecture between on-chip learning and off-chip learning is addressed. This review article provides a foundation for understanding the neuromorphic computing based on the NAND flash memory and methods to utilize it based on application requirements.

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

Compact but precise feature-extracting ability is core to processing complex computational tasks in neuromorphic hardware. Physical reservoir computing (RC) offers a robust framework to map temporal data into a high-dimensional space using the time dynamics of a material system, such as a volatile memristor. However, conventional physical RC systems have limited dynamics for the given material properties, restricting the methods to increase their dimensionality. This study proposes an integrated temporal kernel composed of a 2-memristor and 1-capacitor (2M1C) using a W/HfO2/TiN memristor and TiN/ZrO2/Al2O3/ZrO2/TiN capacitor to achieve higher dimensionality and tunable dynamics. The kernel elements are carefully designed and fabricated into an integrated array, of which performances are evaluated under diverse conditions. By optimizing the time dynamics of the 2M1C kernel, each memristor simultaneously extracts complementary information from input signals. The MNIST benchmark digit classification task achieves a high accuracy of 94.3% with a (196×10) single-layer network. Analog input mapping ability is tested with a Mackey-Glass time series prediction, and the system records a normalized root mean square error of 0.04 with a 20×1 readout network, the smallest readout network ever used for Mackey-Glass prediction in RC. These performances demonstrate its high potential for efficient temporal data analysis.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

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.

Bipolar Resistive Switching in 2D MoSe2 Grown by Atmospheric Pressure Chemical Vapor Deposition

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) are highly promising nanomaterials for various electronic devices such as field-effect transistors, junction diodes, tunneling devices, and, more recently, memristors. 2D MoSe2 stands out for having high electrical conductivity, charge carrier mobility, and melting point. While these features make it particularly appropriate as a switching layer in memristive devices, reliable and scalable production of large-area 2D MoSe2 still represents a challenge. In this study, we manufacture 2D MoSe2 films by atmospheric-pressure chemical vapor deposition and investigate them on the atomic scale. We selected and transferred MoSe2 bilayer to serve as a switching layer between asymmetric Au-Cu electrodes in miniaturized crossbar vertical memristors. The electrochemical metallization devices showed forming-free, bipolar resistive switching at low voltages, with clearly identifiable nonvolatile states. Other than low-power neuromorphic computing, low switching voltages approaching the range of biological action potentials could unlock hybrid biological interfaces.

Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis

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

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.

Revival of Ferroelectric Memories Based on Emerging Fluorite-Structured Ferroelectrics

Over the last few decades, the research on ferroelectric memories has been limited due to their dimensional scalability and incompatibility with complementary metal-oxide-semiconductor (CMOS) technology. The discovery of ferroelectricity in fluorite-structured oxides revived interest in the research on ferroelectric memories, by inducing nanoscale nonvolatility in state-of-the-art gate insulators by minute doping and thermal treatment. The potential of this approach has been demonstrated by the fabrication of sub-30 nm electronic devices. Nonetheless, to realize practical applications, various technical limitations, such as insufficient reliability including endurance, retention, and imprint, as well as large device-to-device-variation, require urgent solutions. Furthermore, such limitations should be considered based on targeting devices as well as applications. Various types of ferroelectric memories including ferroelectric random-access-memory, ferroelectric field-effect-transistor, and ferroelectric tunnel junction should be considered for classical nonvolatile memories as well as emerging neuromorphic computing and processing-in-memory. Therefore, from the viewpoint of materials science, this review covers the recent research focusing on ferroelectric memories from the history of conventional approaches to future prospects.

Complex Oxides for Brain-Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.

Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.

Sol-Gel-Processed Y2O3-Al2O3 Mixed Oxide-Based Resistive Random-Access-Memory Devices

Herein, sol-gel-processed Y2O3-Al2O3 mixed oxide-based resistive random-access-memory (RRAM) devices with different proportions of the involved Y2O3 and Al2O3 precursors were fabricated on indium tin oxide/glass substrates. The corresponding structural, chemical, and electrical properties were investigated. The fabricated devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. With an increase in the percentage of Al2O3 precursor above 50 mol%, the crystallinity reduced, with the amorphous phase increasing owing to internal stress. Moreover, with increasing Al2O3 percentage, the lattice oxygen percentage increased and the oxygen vacancy percentage decreased. A 50% Y2O3-50% Al2O3 mixed oxide-based RRAM device exhibited the maximum high-resistance-state/low-resistance-state (HRS/LRS) ratio, as required for a large readout margin and array size. Additionally, this device demonstrated good endurance characteristics, maintaining stability for approximately 100 cycles with a high HRS/LRS ratio (>104). The HRS and LRS resistances were also retained up to 104 s without considerable degradation.

Sol-Gel-Processed Y2O3 Multilevel Resistive Random-Access Memory Cells for Neural Networks

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol-gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within Y2O3 leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated Y2O3 RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the Y2O3 RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems.

A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure

Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity. However, the multistate retention in the switching conductance range for the long-term reliable neuromorphic system has not been achieved using two-dimensional materials-based ECM memristors. In this study, the copper migration-controlled ECM memristor showing excellent multistate retention characteristics in the switching conductance range using molybdenum disulfide (MoS2 ) and aluminum oxide (Al2 O3 ) is proposed. The fabricated device exhibits gradual resistive switching with low switching voltage (<0.5 V), uniform switching (σ/µ ∼ 0.07), and a wide switching range (>12). Importantly, excellent reliabilities with robustness to cycling stress and retention over 104 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al2 O3 layer to the retention characteristic is investigated through filament morphology observation using transmission electron microscopy (TEM) and copper migration component analysis. This study provides a practical approach to developing highly reliable memristors with exceptional switching performance.

Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning

Multi-terminal memristor and memtransistor (MT-MEMs) has successfully performed complex functions of heterosynaptic plasticity in synapse. However, theses MT-MEMs lack the ability to emulate membrane potential of neuron in multiple neuronal connections. Here, we demonstrate multi-neuron connection using a multi-terminal floating-gate memristor (MT-FGMEM). The variable Fermi level (E_F) in graphene allows charging and discharging of MT-FGMEM using horizontally distant multiple electrodes. Our MT-FGMEM demonstrates high on/off ratio over 10⁵ at 1000 s retention about ~10,000 times higher than other MT-MEMs. The linear behavior between current (I_D) and floating gate potential (V_FG) in triode region of MT-FGMEM allows for accurate spike integration at the neuron membrane. The MT-FGMEM fully mimics the temporal and spatial summation of multi-neuron connections based on leaky-integrate-and-fire (LIF) functionality. Our artificial neuron (150 pJ) significantly reduces the energy consumption by 100,000 times compared to conventional neurons based on silicon integrated circuits (11.7 μJ). By integrating neurons and synapses using MT-FGMEMs, a spiking neurosynaptic training and classification of directional lines functioned in visual area one (V1) is successfully emulated based on neuron's LIF and synapse's spike-timing-dependent plasticity (STDP) functions. Simulation of unsupervised learning based on our artificial neuron and synapse achieves a learning accuracy of 83.08% on the unlabeled MNIST handwritten dataset.

Approaches for Memristive Structures Using Scratching Probe Nanolithography: Towards Neuromorphic Applications

This paper proposes two different approaches to studying resistive switching of oxide thin films using scratching probe nanolithography of atomic force microscopy (AFM). These approaches allow us to assess the effects of memristor size and top-contact thickness on resistive switching. For that purpose, we investigated scratching probe nanolithography regimes using the Taguchi method, which is known as a reliable method for improving the reliability of the result. The AFM parameters, including normal load, scratch distance, probe speed, and probe direction, are optimized on the photoresist thin film by the Taguchi method. As a result, the pinholes with diameter ranged from 25.4 ± 2.2 nm to 85.1 ± 6.3 nm, and the groove array with a depth of 40.5 ± 3.7 nm and a roughness at the bottom of less than a few nanometers was formed. Then, based on the Si/TiN/ZnO/photoresist structures, we fabricated and investigated memristors with different spot sizes and TiN top contact thickness. As a result, the HRS/LRS ratio, U_SET, and I_LRS are well controlled for a memristor size from 27 nm to 83 nm and ranged from ~8 to ~128, from 1.4 ± 0.1 V to 1.8 ± 0.2 V, and from (1.7 ± 0.2) × 10^-10 A to (4.2 ± 0.6) × 10^-9 A, respectively. Furthermore, the HRS/LRS ratio and U_SET are well controlled at a TiN top contact thickness from 8.3 ± 1.1 nm to 32.4 ± 4.2 nm and ranged from ~22 to ~188 and from 1.15 ± 0.05 V to 1.62 ± 0.06 V, respectively. The results can be used in the engineering and manufacturing of memristive structures for neuromorphic applications of brain-inspired artificial intelligence systems.

Recent progress in neuromorphic and memory devices based on graphdiyne

Graphdiyne (GDY) is an emerging two-dimensional carbon allotrope featuring a direct bandgap and fascinating physical and chemical properties, and it has demonstrated its promising potential in applications of catalysis, energy conversion and storage, electrical/optoelectronic devices, etc. In particular, the recent breakthrough in the synthesis of large-area, high-quality and ultrathin GDY films provides a feasible approach to developing high-performance electrical devices based on GDY. Recently, various GDY-based electrical and optoelectronic devices including multibit optoelectronic memories, ultrafast nonvolatile memories, artificial synapses and memristors have been proposed, in which GDY plays a crucial role. It is essential to summarize the recent breakthrough of GDY in device applications as a guidance, especially considering that the existing GDY-related reviews mainly focus on the applications in catalysis and energy-related fields. Herein, we review GDY-based novel memory and neuromorphic devices and their applications in neuromorphic computing and artificial visual systems. This review will provide an insight into the design and preparation of GDY-based devices and broaden the application fields of GDY.

A photonic artificial synapse with a reversible multifaceted photochromic compound

Modern computational technology based on the von Neumann architecture physically partitions memory and the central processing unit, resulting in fundamental speed limitations and high energy consumption. On the other hand, the human brain is an extraordinary multifunctional organ composed of more than a billion neurons capable of simultaneously thinking, processing, and storing information. Neurons are interconnected with synapses that control information flow from pre-synaptic-to-post-synaptic neurons. Therefore, emulating synaptic functionalities and developing neuromorphic computational architecture has recently attracted much interest. Due to their high-speed, large bandwidth, and no interconnect-related power loss, photonic (all-optical) synapses can overcome the existing hurdles with electronic synapses. Here, we show an artificial photonic synapse by utilizing the well-established reversible, high-contrast photochromic organic compound, spiropyran, stimulated by optical pulses. Optical transmission of spiropyran significantly changes during spiropyran-merocyanine isomerization driven by UV-visible optical pulses. Such changes are equivalent to the biological synapses' inhibitory and excitatory synaptic actions. The slow relaxation to the initial state is considered as synaptic plasticity responsible for learning and memory formation. Short-term memory (STM), long-term memory (LTM), and transition from the STM to the LTM are demonstrated in all-optical synapses by modulating the stimuli's strength. The solvatochromic properties of spiropyran are further utilized to augment memory in synapses. Our work shows that photochromic organic compounds are excellent hosts for artificial photonic synapses and can be implemented in neuromorphic applications.

Evaluating charge-type of polyelectrolyte as dielectric layer in memristor and synapse emulation

Based on credible advantages, organic materials have received more and more attention in memristor and synapse emulation. In particular, an implementation of the ionic pathway as a dielectric layer is essential for organic materials used as building blocks of memristor and artificial synaptic devices. Herein, we describe an evaluation of the use of positive and negative polyelectrolytes as dielectric layers for a memristor with calcium ion (Ca²⁺) doping. The device based on a negative polyelectrolyte shows the potential to obtain an excellent resistive switching performance and synapse functionality, especially in the transformation behaviours from short-term plasticity (STP) to long-term plasticity (LTP) in both the potentiation and depression processes, which were comparable to the perfomrmance obtained with a positive polyelectrolyte. The mechanism of electrical resistance transition and synaptic function can be attributed to the migration of the doped Ca²⁺ and the ionic functional groups of polyelectrolyte, which result in the formation and vanishing filament-like Ca²⁺ flux.

Graph Analysis with Multifunctional Self-Rectifying Memristive Crossbar Array

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

Echo state graph neural networks with analogue random resistive memory arrays

Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conventional digital hardware, including the slowdown of Moore’s law due to transistor scaling limits and the von Neumann bottleneck incurred by physically separated memory and processing units, as well as a high training cost. Here we present a hardware–software co-design to address these challenges, by designing an echo state graph neural network based on random resistive memory arrays, which are built from low-cost, nanoscale and stackable resistors for efficient in-memory computing. This approach leverages the intrinsic stochasticity of dielectric breakdown in resistive switching to implement random projections in hardware for an echo state network that effectively minimizes the training complexity thanks to its fixed and random weights. The system demonstrates state-of-the-art performance on both graph classification using the MUTAG and COLLAB datasets and node classification using the CORA dataset, achieving 2.16×, 35.42× and 40.37× improvements in energy efficiency for a projected random resistive memory-based hybrid analogue–digital system over a state-of-the-art graphics processing unit and 99.35%, 99.99% and 91.40% reductions of backward pass complexity compared with conventional graph learning. The results point to a promising direction for next-generation artificial intelligence systems for graph learning.

Improved Environment Stability of Y2O3 RRAM Devices with Au Passivated Ag Top Electrodes

In this study, we fabricated sol-gel-processed Y2O3-based resistive random-access memory (RRAM) devices. The fabricated Y2O3 RRAM devices exhibited conventional bipolar RRAM device characteristics and did not require the forming process. The long-term stability of the RRAM devices was investigated. The Y2O3 RRAM devices with a 20 nm thick Ag top electrode showed an increase in the low resistance state (LRS) and high resistance state (HRS) and a decrease in the HRS/LRS ratio after 30 days owing to oxidation and corrosion of the Ag electrodes. However, Y2O3 RRAM devices with inert Au-passivated Ag electrodes showed a constant RRAM device performance after 30 days. The 150 nm-thick Au passivation layer successfully suppressed the oxidation and corrosion of the Ag electrode by minimizing the chance of contact between water or oxygen molecules and Ag electrodes. The Au/Ag/Y2O3/ITO RRAM devices exhibited more than 300 switching cycles with a decent resistive window (>103). They maintained constant LRS and HRS resistances for up to 104 s, without significant degradation of nonvolatile memory properties for 30 days while stored in air.

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu_0.1Te_0.9/HfO₂/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization.

Designing a computing scheme to solve complex tasks as the big data field proliferates remains a challenge. Here, the authors present a probabilistic bit generation hardware built using the random nature of Cu_xTe_1−x/HfO₂/Pt memristors capable of performing logic gates with invertible mode, showing the expandability to complex logic circuits.

Multivalued Logic for Optical Computing with Photonically Enabled Chiral Bio-organic Structures

Photonic bio-organic multiphase structures are suggested here for integrated thin-film electronic nets with multilevel logic elements for multilevel computing via a reconfigurable photonic bandgap of chiral biomaterials. Herein, inspired by an artificial intelligence system with efficient information integration and computing capability, the photonically active dielectric layer of chiral nematic cellulose nanocrystals is combined with printed-in p- and n-type organic semiconductors as a bifunctional logical element. These adaptive logic elements are capable of triggering tailored quantized electrical output signals under light with different photon energy and at the different photonic bandgaps of the active dielectric layer. The bifunctional structures enable complex memory behavior upon repetitive changes of photonic bandgap (controlled by expansion/contraction of chiral nematic pitch) and photon energy (controlled by light absorption wavelength of complementary organic semiconductor layers), exhibiting effectively a reconfigurable ternary logic response. This proof-of-concept bio-assisted multivalued logic structure facilitates an optical computing system for low-power optical information processing integrated with human-machine interfaces.

Reversible electrical percolation in a stretchable and self-healable silver-gradient nanocomposite bilayer

The reversibly stable formation and rupture processes of electrical percolative pathways in organic and inorganic insulating materials are essential prerequisites for operating non-volatile resistive memory devices. However, such resistive switching has not yet been reported for dynamically cross-linked polymers capable of intrinsic stretchability and self-healing. This is attributable to the uncontrollable interplay between the conducting filler and the polymer. Herein, we present the development of the self-healing, stretchable, and reconfigurable resistive random-access memory. The device was fabricated via the self-assembly of a silver-gradient nanocomposite bilayer which is capable of easily forming the metal-insulator-metal structure. To realize stable resistive switching in dynamic molecular networks, our device features the following properties: i) self-reconstruction of nanoscale conducting fillers in dynamic hydrogen bonding for self-healing and reconfiguration and ii) stronger interaction among the conducting fillers than with polymers for the formation of robust percolation paths. Based on these unique features, we successfully demonstrated stable data storage of cardiac signals, damage-reliable memory triggering system using a triboelectric energy-harvesting device, and touch sensing via pressure-induced resistive switching.

Neuromorphic device based on silicon nanosheets

Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations.

Controlled Formation of Conduction Channels in Memristive Devices Observed by X-ray Multimodal Imaging

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. This work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

Enhanced Switching Reliability of Sol–Gel-Processed Y2O3 RRAM Devices Based on Y2O3 Surface Roughness-Induced Local Electric Field

Sol–gel-processed Y₂O₃ films were used as active channel layers for resistive random access memory (RRAM) devices. The fabricated ITO/Y₂O₃/Ag RRAM devices exhibited the properties of conventional bipolar memory devices. A triethylamine stabilizer with a high vapor pressure and low surface tension was added to realize the local electric field area. During drying and high-temperature post-annealing processes, the large convective flow enhanced the surface elevation, and the increased –OH groups accelerated the hydrolysis reaction and aggregation. These phenomena afforded Y₂O₃ films with an uneven surface morphology and an increased surface roughness. The increased roughness of the Y₂O₃ films attributable to the triethylamine stabilizer enhanced the local electrical field, improved device reliability, and achieved successful repetition of the switching properties over an extended period.

Flexible Sol-Gel-Processed Y2O3 RRAM Devices Obtained via UV/Ozone-Assisted Photochemical Annealing Process

Flexible indium tin oxide (ITO)/Y2O3/Ag resistive random access memory (RRAM) devices were successfully fabricated using a thermal-energy-free ultraviolet (UV)/ozone-assisted photochemical annealing process. Using the UV/ozone-assisted photochemical process, the organic residue can be eliminated, and thinner and smother Y2O3 films than those formed using other methods can be fabricated. The flexible UV/ozone-assisted photochemical annealing process-based ITO/Y2O3/Ag RRAM devices exhibited the properties of conventional bipolar RRAM without any forming process. Furthermore, the pure and amorphous-phase Y2O3 films formed via this process showed a decreased leakage current and an increased high-resistance status (HRS) compared with the films formed using other methods. Therefore, RRAM devices can be realized on plastic substrates using a thermal-energy-free UV/ozone-assisted photochemical annealing process. The fabricated devices exhibited a resistive window (ratio of HRS/low-resistance status (LRS)) of >104, with the HRS and LRS values remaining almost the same (i.e., limited deterioration occurred) for 104 s and up to 102 programming/erasing operation cycles.

A Memristive Circuit Implementation of Eyes State Detection in Fatigue Driving Based on Biological Long Short-Term Memory Rule

Biological long short-term memory (B-LSTM) can effectively help human process all kinds of received information. In this work, a memristive B-LSTM circuit which mimics a conversion from short-term memory to long-term memory is proposed. That is, the stronger the signal, the more profound the memory and the higher the output. On this basis, an image binarization circuit using adaptive row threshold algorithm is proposed. It can make the image remain a deep impression on the strong pixel information and effectively filter the relatively weak pixel information. In combination with the function of image binarization, a memristive circuit for eyes state detection is proposed by adding corresponding horizontal projection calculation, subtraction calculation and judgement open or closed eyes modules. The proposed circuit can detect whether there is a blink between two adjacent facial images, which uses the characteristics of memristor to detect the difference of horizontal projection between two images. Due to the use of memristor, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and shorten the execution time. Finally, an expectation application in fatigue driving based on the proposed method is demonstrated, which indicates the practicability of the circuit design in this work.

ROA: A Rapid Learning Scheme for In-Situ Memristor Networks

Memristors show great promise in neuromorphic computing owing to their high-density integration, fast computing and low-energy consumption. However, the non-ideal update of synaptic weight in memristor devices, including nonlinearity, asymmetry and device variation, still poses challenges to the in-situ learning of memristors, thereby limiting their broad applications. Although the existing offline learning schemes can avoid this problem by transferring the weight optimization process into cloud, it is difficult to adapt to unseen tasks and uncertain environments. Here, we propose a bi-level meta-learning scheme that can alleviate the non-ideal update problem, and achieve fast adaptation and high accuracy, named Rapid One-step Adaption (ROA). By introducing a special regularization constraint and a dynamic learning rate strategy for in-situ learning, the ROA method effectively combines offline pre-training and online rapid one-step adaption. Furthermore, we implemented it on memristor-based neural networks to solve few-shot learning tasks, proving its superiority over the pure offline and online schemes under noisy conditions. This method can solve in-situ learning in non-ideal memristor networks, providing potential applications of on-chip neuromorphic learning and edge computing.

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

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

Artificial Synapses Based on Lead-Free Perovskite Floating-Gate Organic Field-Effect Transistors for Supervised and Unsupervised Learning

Synaptic devices are expected to overcome von Neumann's bottleneck and served as one of the foundations for future neuromorphic computing. Lead halide perovskites are considered as promising photoactive materials but limited by the toxicity of lead. Herein, lead-free perovskite CsBi₃I₁₀ is utilized as a photoactive material to fabricate organic synaptic transistors with a floating-gate structure for the first time. The devices can maintain the I_light/I_dark ratio of 10³ for 4 h and have excellent stability within the 30 days test even without encapsulation. Synaptic functions are successfully simulated. Notably, by combining the decent charge transport property of the organic semiconductor and the excellent photoelectronic property of CsBi₃I₁₀, synaptic performance can be realized even with an operating voltage as low as -0.01 V, which is rare among floating-gate synaptic transistors. Furthermore, artificial neural networks are constructed. We propose a new method that can simulate the synaptic weight value in multiple digit form to achieve complete gradient descent. The image recognition test exhibits thrilling recognition accuracy for both supervised (91%) and unsupervised (81%) classifications. These results demonstrate the great potential of floating-gate organic synaptic transistors in neuromorphic computing.

Phase Transitions in a Perovskite Thin Film Studied by Environmental In Situ Heating Nano-Beam Electron Diffraction

The rich phase diagram of bulk Pr_1-x Ca_x MnO₃ resulting in a high tunability of physical properties gives rise to various studies related to fundamental research as well as prospective applications of the material. Importantly, as a consequence of strong correlation effects, electronic and lattice degrees of freedom are vigorously coupled. Hence, it is debatable whether such bulk phase diagrams can be transferred to inherently strained epitaxial thin films. In this paper, the structural orthorhombic to pseudo-cubic transition for x = 0.1 is studied in ion-beam sputtered thin films and differences to the respective bulk system are pointed out by employing in situ heating nano-beam electron diffraction to follow the temperature dependence of lattice constants. In addition, it is demonstrated that controlling the environment during heating, that is, preventing oxygen loss, is crucial in order to avoid irreversible structural changes, which is expected to be a general problem of compounds containing volatile elements under non-equilibrium conditions.

Area-Type Electronic Bipolar Switching Al/TiO1.7/TiO2/Al Memory with Linear Potentiation and Depression Characteristics

In electronic bipolar resistive switching (eBRS), the electron trapping and detrapping at the defect sites within the switching layer, such as the highly defective TiO_1.7 in this study, constitute the switching mechanism. It is an appealing candidate solution to the nonuniformity issue of resistive switching memory. However, TiO_1.7-based eBRS has suffered from a lack of endurance and retention. In this study, a 7 nm-thick stoichiometric TiO₂ layer is interposed between an Al bottom electrode and a 50 nm-thick TiO_1.7 layer, which is in contact with an Al top electrode. Despite the minimal structural modification, improvements in the electrical performance were substantial. The off-to-on state resistance ratio of 20 and the resistance values could be retained up to 30 000 direct current sweep cycles and 10⁶ alternating current pulse switching cycles. Data retention also significantly improves. Moreover, the device is electroforming-free and shows fully area-type switching characteristics. Such notable improvements are attributed to the favorable energy band structure of the Al/TiO_1.7/TiO₂/Al structure. The device shows almost linear potentiation and depression characteristics after the repeated pulse voltage applications, which significantly improves the accuracy of the neural network, the synapses of which are composed of the Al/TiO_1.7/TiO₂/Al memory cells.

Wide range modulation of synaptic weight in thin-film transistors with hafnium oxide gate insulator and indium-zinc oxide channel layer for artificial synapse application

Wide range synaptic weight modulation with a tunable drain current was demonstrated in thin-film transistors (TFTs) with a hafnium oxide (HfO2-x) gate insulator and an indium-zinc oxide (IZO) channel layer for application to artificial synapses in neuromorphic systems. The drain current in these TFTs was reduced significantly by four orders of magnitude on application of a negative gate bias, then could be restored to its original value by applying a positive bias. The reduced drain current under negative biasing is interpreted as being caused by voltage-driven oxygen ion migration from the HfO2-x gate insulator to the IZO channel, which reduces the oxygen vacancy concentration in the IZO channel. In addition to emulating the analog-type potentiation and depression motions in artificial synapses, the tunable drain current presents paired-pulse facilitation and short-term and long-term plasticity behaviors. These wide-ranging and nonvolatile synaptic behaviors with tunable drain currents are indicative of the potential of the proposed TFTs for artificial synapse applications.

Metal chalcogenides for neuromorphic computing: emerging materials and mechanisms

The approaching end of Moore's law scaling has significantly accelerated multiple fields of research including neuromorphic-, quantum-, and photonic computing, each of which possesses unique benefits unobtained through conventional binary computers. One of the most compelling arguments for neuromorphic computing systems is power consumption, noting that computations made in the human brain are approximately 10⁶times more efficient than conventional CMOS logic. This review article focuses on the materials science and physical mechanisms found in metal chalcogenides that are currently being explored for use in neuromorphic applications. We begin by reviewing the key biological signal generation and transduction mechanisms within neuronal components of mammalian brains and subsequently compare with observed experimental measurements in chalcogenides. With robustness and energy efficiency in mind, we will focus on short-range mechanisms such as structural phase changes and correlated electron systems that can be driven by low-energy stimuli, such as temperature or electric field. We aim to highlight fundamental materials research and existing gaps that need to be overcome to enable further integration or advancement of metal chalcogenides for neuromorphic systems.

Emerging Opportunities for 2D Semiconductor/Ferroelectric Transistor-Structure Devices

Semiconductor technology, which is rapidly evolving, is poised to enter a new era for which revolutionary innovations are needed to address fundamental limitations on material and working principle level. 2D semiconductors inherently holding novel properties at the atomic limit show great promise to tackle challenges imposed by traditional bulk semiconductor materials. Synergistic combination of 2D semiconductors with functional ferroelectrics further offers new working principles, and is expected to deliver massively enhanced device performance for existing complementary metal-oxide-semiconductor (CMOS) technologies and add unprecedented applications for next-generation electronics. Herein, recent demonstrations of novel device concepts based on 2D semiconductor/ferroelectric heterostructures are critically reviewed covering their working mechanisms, device construction, applications, and challenges. In particular, emerging opportunities of CMOS-process-compatible 2D semiconductor/ferroelectric transistor structure devices for the development of a rich variety of applications are discussed, including beyond-Boltzmann transistors, nonvolatile memories, neuromorphic devices, and reconfigurable nanodevices such as p-n homojunctions and self-powered photodetectors. It is concluded that 2D semiconductor/ferroelectric heterostructures, as an emergent heterogeneous platform, could drive many more exciting innovations for modern electronics, beyond the capability of ubiquitous silicon systems.

Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system

Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips (i.e., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS₂ and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS₂-organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision.

Designing efficient bio-inspired visual recognition system remains a challenge. Here the authors present a curved neuromorphic image sensor array based on a heterostructure of MoS2 and pV3D3 integrated with a plano-convex lens for efficient image acquisition and data pre-processing.

Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices

Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.

Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells.

Conductive-bridging random-access memories for emerging neuromorphic computing

With the increasing utilisation of artificial intelligence, there is a renewed demand for the development of novel neuromorphic computing owing to the drawbacks of the existing computing paradigm based on the von Neumann architecture. Extensive studies have been performed on memristors as their electrical nature is similar to those of biological synapses and neurons. However, most hardware-based artificial neural networks (ANNs) have been developed with oxide-based memristors owing to their high compatibility with mature complementary metal-oxide-semiconductor (CMOS) processes. Considering the advantages of conductive-bridging random-access memories (CBRAMs), such as their high scalability, high on-off current with a wide dynamic range, and low off-current, over oxide-based memristors, extensive studies on CBRAMs are required. In this review, the basics of operation of CBRAMs are examined in detail, from the formation of metal nanoclusters to filament bridging. Additionally, state-of-the-art experimental demonstrations of CBRAM-based artificial synapses and neurons are presented. Finally, CBRAM-based ANNs are discussed, including deep neural networks and spiking neural networks, along with other emerging computing applications. This review is expected to pave the way toward further development of large-scale CBRAM array systems.

Novel hardware and concepts for unconventional computing

Neuromorphic systems are currently experiencing a rapid upswing due to the fact that today's CMOS (complementary metal oxide silicon) based technologies are increasingly approaching their limits. In particular, for the area of machine learning, energy consumption of today's electronics is an important limitation, that also contributes toward the ever-increasing impact of digitalization on our climate. Thus, in order to better meet the special requirements of unconventional computing, new physical substrates for bio-inspired computing schemes are extensively exploited. The aim of this Guest Edited Collection is to provide a platform for interdisciplinary research along three main lines: memristive materials and devices, emulation of cellular learning (neurons and synapses), and unconventional computing and network schemes.

One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications

One-dimensional (1D) devices are becoming the most desirable format for wearable electronic technology because they can be easily woven into electronic (e-) textile(s) with versatile functional units while maintaining their inherent features under mechanical stress. In this study, we designed 1D fiber-shaped multi-synapses comprising ferroelectric organic transistors fabricated on a 100-μm Ag wire and used them as multisynaptic channels in an e-textile neural network for wearable neuromorphic applications. The device mimics diverse synaptic functions with excellent reliability even under 6000 repeated input stimuli and mechanical bending stress. Various NOR-type textile arrays are formed simply by cross-pointing 1D synapses with Ag wires, where each output from individual synapse can be integrated and propagated without undesired leakage. Notably, the 1D multi-synapses achieved up to ~90 and ~70% recognition accuracy for MNIST and electrocardiogram patterns, respectively, even in a single-layer neural network, and almost maintained regardless of the bending conditions.

TEM Nanostructural Investigation of Ag-Conductive Filaments in Polycrystalline ZnO-Based Resistive Switching Devices

Memristive devices based on a resistive switching mechanism are considered very promising for nonvolatile memory and unconventional computing applications, even though many details of the switching mechanisms are not yet fully understood. Here, we report a nanostructural study by means of high-resolution transmission electron microscopy and spectroscopy techniques of a Ag/ZnO/Pt memristive device. To ease the localization of the filament position for its characterization, we propose to use the guiding effect of regular perturbation arrays obtained by FIB technology to assist the filament formation. HRTEM and EDX were used to identify the composition and crystalline structure of the so-obtained conductive filaments and surrounding regions. It was determined that the conducting paths are composed mainly of monocrystalline Ag, which remains polycrystalline in some circumstances, including the zone where the switching occurs and at secondary filaments created at the grain boundaries of the polycrystalline ZnO matrix. We also observed that the ZnO matrix shows a degraded quality in the switching zone, while it remains unaltered in the rest of the memristive device.

Exchange of Ions across the TiN/TaOx Interface during Electroformation of TaOx-Based Resistive Switching Devices

The valence change model describes the resistive switching in metal oxide-based devices as due to electroreduction of the oxide and subsequent electromigration of oxygen vacancies. Here, we present cross-sectional X-ray energy-dispersive spectroscopy elemental maps of Ta, O, N, and Ti in electroformed TiN/TaO_2.0/TiN structures. O, N, and Ti were exchanged between the anode and the functional oxide in devices formed at high power (∼1 mW), but the exchange was below the detection limit at low power (<0.5 mW). All structures exhibit a similar Ta-enriched and O-depleted filament formed by the elemental segregation in the functional oxide by the temperature gradient. The elemental interchange is interpreted as due to Fick's diffusion caused by high temperatures in the gap of the filament and is not an essential part of electroformation.

Multi-Bit Biomemory Based on Chitosan: Graphene Oxide Nanocomposite with Wrinkled Surface

Chitosan (CS) is one of the commonly affluent polysaccharides that are attractive biomaterials as they are easily found in different organisms and are biocompatible. An environment-friendly multi-bit biomemory was successfully achieved on the basis of CS as a favorable candidate for resistive-switching memory applications. By incorporating graphene oxide (GO) into CS, the multi-bit biomemory device (indium tin oxide (ITO)/CS:GO/Ni) was obtained through the solution-processable method, which had a high current ratio among a high, intermediate, and low resistance state as well as a low SET/RESET voltage. GO acting as trapping sites in the active layer might be responsible for the biomemory mechanism. This research opens up a new avenue towards renewable and environmentally benign CS-based materials for biodegradable electronic devices.