Memristive technologies for data storage, computation, encryption, and radio-frequency communication

Promotion of Probabilistic Bit Generation in Mott Devices by Embedded Metal Nanoparticles

Considerable attention has been drawn to the use of volatile two-terminal devices relying on the Mott transition for the stochastic generation of probabilistic bits (p-bits) in emerging probabilistic computing. To improve randomness and endurance of bit streams provided by these devices, delicate control of the transient evolution of switchable domains is required to enhance stochastic p-bit generation. Herein, it is demonstrated that the randomness of p-bit streams generated via the consecutive pulse inputs of pump-probe protocols can be increased by the deliberate incorporation of metal nanoparticles (NPs), which influence the transient dynamics of the nanoscale metallic phase in VO2 Mott switches. Among the vertically stacked Pt-NP-containing VO2 threshold switches, those with higher Pt NP density show a considerably wider range of p-bit operation (e.g., up to ≈300% increase in ΔVprobe upon going from (Pt NP/VO2)0 to (Pt NP/VO2)11) and can therefore be operated under the conditions of high speed (400 kbit s-1), low power consumption (14 nJ per bit), and high stability (>105 200 bits) for p-bit generation. Thus, the study presents a novel strategy that exploits nanoscale phase control to maximize the generation of nondeterministic information sources for energy-efficient probabilistic computing hardware.

A memristive-photoconductive transduction methodology for accurately nondestructive memory readout

Crossbar resistive memory architectures enable high-capacity storage and neuromorphic computing, accurate retrieval of the stored information is a prerequisite during read operation. However, conventional electrical readout normally suffer from complicated process, inaccurate and destructive reading due to crosstalk effect from sneak path current. Here we report a memristive-photoconductive transduction (MPT) methodology for precise and nondestructive readout in a memristive crossbar array. The individual devices present dynamic filament form/fuse for resistance modulation under electric stimulation, which leads to photogenerated carrier transport for tunable photoconductive response under subsequently light pulse stimuli. This coherent signal transduction can be used to directly detect the memorized on/off states stored in each cell, and a prototype 4 * 4 crossbar memories has been constructed and validated for the fidelity of crosstalk-free readout in recall process.

An enzymolysis-induced energy transfer co-assembled system for spontaneously recoverable supramolecular dynamic memory

The continuing growth of the digital world requires new ways of constructing memory devices to process and store dynamic data, because the current ones suffer from inefficiency, limited reads, and difficulty to manufacture. Here we propose a supramolecular dynamic memory (SDM) strategy based on an enzymolysis-induced energy transfer co-assembly derived from a naphthalene-based cationic monomer and organic dye sulforhodamine 101, enabling the construction of spontaneously recoverable dynamic memory devices. Benefitting from the large exciton migration rate (4.48 × 1015 L mol-1 s-1) between the monomer and sulforhodamine 101, the energy transfer process between the two is effectively achieved. Since alkaline phosphatase can selectively hydrolyze adenosine triphosphate, leading to the disruption of the co-assemblies, an enzyme-mediated time-dependent fluorochromic system is realized. On this basis, a SDM system featuring spontaneous recovery and enabling the memory of dynamic information in optical and electrical modes is successfully constructed. The current study represents a promising step in the nascent development of supramolecular materials for computational systems.

Voltage control of multiferroic magnon torque for reconfigurable logic-in-memory

Magnons, bosonic quasiparticles carrying angular momentum, can flow through insulators for information transmission with minimal power dissipation. However, it remains challenging to develop a magnon-based logic due to the lack of efficient electrical manipulation of magnon transport. Here we show the electric excitation and control of multiferroic magnon modes in a spin-source/multiferroic/ferromagnet structure. We demonstrate that the ferroelectric polarization can electrically modulate the magnon-mediated spin-orbit torque by controlling the non-collinear antiferromagnetic structure in multiferroic bismuth ferrite thin films with coupled antiferromagnetic and ferroelectric orders. In this multiferroic magnon torque device, magnon information is encoded to ferromagnetic bits by the magnon-mediated spin torque. By manipulating the two coupled non-volatile state variables-ferroelectric polarization and magnetization-we further present reconfigurable logic operations in a single device. Our findings highlight the potential of multiferroics for controlling magnon information transport and offer a pathway towards room-temperature voltage-controlled, low-power, scalable magnonics for in-memory computing.

Reconfigurable spintronic logic gate utilizing precessional magnetization switching

In traditional von Neumann computing architecture, the efficiency of the system is often hindered by the data transmission bottleneck between the processor and memory. A prevalent approach to mitigate this limitation is the use of non-volatile memory for in-memory computing, with spin-orbit torque (SOT) magnetic random-access memory (MRAM) being a leading area of research. In this study, we numerically demonstrate that a precise combination of damping-like and field-like spin-orbit torques can facilitate precessional magnetization switching. This mechanism enables the binary memristivity of magnetic tunnel junctions (MTJs) through the modulation of the amplitude and width of input current pulses. Building on this foundation, we have developed a scheme for a reconfigurable spintronic logic gate capable of directly implementing Boolean functions such as AND, OR, and XOR. This work is anticipated to leverage the sub-nanosecond dynamics of SOT-MRAM cells, potentially catalyzing further experimental developments in spintronic devices for in-memory computing.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

New-Generation Ferroelectric AlScN Materials

Ferroelectrics have great potential in the field of nonvolatile memory due to programmable polarization states by external electric field in nonvolatile manner. However, complementary metal oxide semiconductor compatibility and uniformity of ferroelectric performance after size scaling have always been two thorny issues hindering practical application of ferroelectric memory devices. The emerging ferroelectricity of wurtzite structure nitride offers opportunities to circumvent the dilemma. This review covers the mechanism of ferroelectricity and domain dynamics in ferroelectric AlScN films. The performance optimization of AlScN films grown by different techniques is summarized and their applications for memories and emerging in-memory computing are illustrated. Finally, the challenges and perspectives regarding the commercial avenue of ferroelectric AlScN are discussed.

Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures

Hybrid halide perovskites are attractive candidates for resistive switching memories in neuromorphic computing applications due to their mixed ionic-electronic conductivity. Moreover, their exceptional optoelectronic characteristics make them effective as semiconductors in photovoltaics, opening perspectives for self-powered memory elements. These devices, however, remain unexploited, which is related to the variability in their switching characteristics, weak endurance, and retention, which limit their performance and practical use. To address this challenge, we applied low-dimensional perovskite capping layers onto 3D mixed halide perovskites using two perfluoroarene organic cations, namely (perfluorobenzyl)ammonium and (perfluoro-1,4-phenylene)dimethylammonium iodide, forming Ruddlesden-Popper and Dion-Jacobson 2D perovskite phases, respectively. The corresponding mixed-dimensional perovskite heterostructures were used to fabricate resistive switching memories based on perovskite solar cell architectures, showing that the devices based on perfluoroarene heterostructures exhibited enhanced performance and stability in inert and ambient air atmosphere. This opens perspectives for multidimensional perovskite materials in durable self-powered memory elements in the future.

Memristive Control of Plasmon-Mediated Nonlinear Photoluminescence in Au Nanowires

Nonlinear photoluminescence (N-PL) is a broadband photon emission arising from a nonequilibrium heated electron distribution generated at the surface of metallic nanostructures by ultrafast pulsed laser illumination. N-PL is sensitive to surface morphology, local electromagnetic field strength, and electronic band structure, making it relevant to probe optically excited nanoscale plasmonic systems. It also has been key to accessing the complex multiscale time dynamics ruling electron thermalization. Here, we show that plasmon-mediated N-PL emitted by a gold nanowire can be modified by an electrical architecture featuring a nanogap. Upon voltage activation, we observe that N-PL becomes dependent on the electrical transport dynamics and can thus be locally modulated. This finding brings an electrical leverage to externally control the photoluminescence generated from metal nanostructures and constitutes an asset for the development of emerging nanoscale interface devices managing photons and electrons.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems

Memristive neuromorphic computing has emerged as a promising computing paradigm for the upcoming artificial intelligence era, offering low power consumption and high speed. However, its commercialization remains challenging due to reliability issues from stochastic ion movements. Here, we propose an innovative method to enhance the memristive uniformity and performance through aliovalent halide doping. By introducing fluorine concentration into dynamic TiO2-x memristors, we experimentally demonstrate reduced device variations, improved switching speeds, and enhanced switching windows. Atomistic simulations of amorphous TiO2-x reveal that fluoride ions attract oxygen vacancies, improving the reversible redistribution and uniformity. A number of migration barrier calculations statistically show that fluoride ions also reduce the migration energies of nearby oxygen vacancies, facilitating ionic diffusion and high-speed switching. The detailed Voronoi volume analysis further suggests design principles in terms of the migrating species' electrostatic repulsion and migration barriers. This work presents an innovative methodology for the fabrication of reliable memristor devices, contributing to the realization of hardware-based neuromorphic systems.

Variability in HfO2-based memristors described with a new bidimensional statistical technique

A new statistical analysis is presented to assess cycle-to-cycle variability in resistive memories. This method employs two-dimensional (2D) distributions of parameters to analyse both set and reset voltages and currents, coupled with a 2D coefficient of variation (CV). This 2D methodology significantly enhances the analysis, providing a more thorough and comprehensive understanding of the data compared to conventional one-dimensional methods. Resistive switching (RS) data from two different technologies based on hafnium oxide are used in the variability study. The 2D CV allows a more compact assessment of technology suitability for applications such as non-volatile memories, neuromorphic computing and random number generation circuits.

Domain Switching Characteristics in Ga-Doped HfO2 Ferroelectric Thin Films with Low Coercive Field

The gallium-doped hafnium oxide (Ga-HfO2) films with different Ga doping concentrations were prepared by adjusting the HfO2/Ga2O3 atomic layer deposition cycle ratio for high-speed and low-voltage operation in HfO2-based ferroelectric memory. The Ga-HfO2 ferroelectric films reveal a finely modulated coercive field (Ec) from 1.1 (HfO2/Ga2O3 = 32:1) to an exceptionally low 0.6 MV/cm (HfO2/Ga2O3 = 11:1). This modulation arises from the competition between domain nucleation and propagation speed during polarization switching, influenced by the intrinsic domain density and phase dispersion in the film with specific Ga doping concentrations. Higher Ec samples exhibit a nucleation-dominant switching mechanism, while lower Ec samples undergo a transition from a nucleation-dominant to a propagation-dominant reversal mechanism as the electric field increases. This work introduces Ga as a viable dopant for low Ec and offers insights into material design strategies for HfO2-based ferroelectric memory applications.

Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems

In this study, we investigate the coexistence of short- and long-term memory effects owing to the programmable retention characteristics of a two-dimensional Au/MoS2/Au atomristor device and determine the impact of these effects on synaptic properties. This device is constructed using bilayer MoS2 in a crossbar structure. The presence of both short- and long-term memory characteristics is proposed by using a filament model within the bilayer transition-metal dichalcogenide. Short- and long-term properties are validated based on programmable multilevel retention tests. Moreover, we confirm various synaptic characteristics of the device, demonstrating its potential use as a synaptic device in a neuromorphic system. Excitatory postsynaptic current, paired-pulse facilitation, spike-rate-dependent plasticity, and spike-number-dependent plasticity synaptic applications are implemented by operating the device at a low-conductance level. Furthermore, long-term potentiation and depression exhibit symmetrical properties at high-conductance levels. Synaptic learning and forgetting characteristics are emulated using programmable retention properties and composite synaptic plasticity. The learning process of artificial neural networks is used to achieve high pattern recognition accuracy, thereby demonstrating the suitability of the use of the device in a neuromorphic system. Finally, the device is used as a physical reservoir with time-dependent inputs to realize reservoir computing by using short-term memory properties. Our study reveals that the proposed device can be applied in artificial intelligence-based computing applications by utilizing its programmable retention properties.

Single-Pore Nanofluidic Logic Memristor with Reconfigurable Synaptic Functions and Designable Combinations

The biological neural network is a highly efficient in-memory computing system that integrates memory and logical computing functions within synapses. Moreover, reconfiguration by environmental chemical signals endows biological neural networks with dynamic multifunctions and enhanced efficiency. Nanofluidic memristors have emerged as promising candidates for mimicking synaptic functions, owing to their similarity to synapses in the underlying mechanisms of ion signaling in ion channels. However, realizing chemical signal-modulated logic functions in nanofluidic memristors, which is the basis for brain-like computing applications, remains unachieved. Here, we report a single-pore nanofluidic logic memristor with reconfigurable logic functions. Based on the different degrees of protonation and deprotonation of functional groups on the inner surface of the single pore, the modulation of the memristors and the reconfiguration of logic functions are realized. More noteworthy, this single-pore nanofluidic memristor can not only avoid the average effects in multipore but also act as a fundamental component in constructing complex neural networks through series and parallel circuits, which lays the groundwork for future artificial nanofluidic neural networks. The implementation of dynamic synaptic functions, modulation of logic gates by chemical signals, and diverse combinations in single-pore nanofluidic memristors opens up new possibilities for their applications in brain-inspired computing.

Oxygen tracer diffusion in amorphous hafnia films for resistive memory

The oxygen diffusion rate in hafnia (HfO2)-based resistive memory plays a pivotal role in enabling nonvolatile data retention. However, the information retention times obtained in HfO2 resistive memory devices are many times higher than the expected values obtained from oxygen diffusion measurements in HfO2 materials. In this study, we resolve this discrepancy by conducting oxygen isotope tracer diffusion measurements in amorphous hafnia (a-HfO2) thin films. Our results show that the oxygen tracer diffusion in amorphous HfO2 films is orders of magnitude lower than that of previous measurements on monoclinic hafnia (m-HfO2) pellets. Moreover, oxygen tracer diffusion is much lower in denser a-HfO2 films deposited by atomic layer deposition (ALD) than in less dense a-HfO2 films deposited by sputtering. The ALD films yield similar oxygen diffusion times as experimentally measured device retention times, reconciling this discrepancy between oxygen diffusion and retention time measurements. More broadly, our work shows how processing conditions can be used to control oxygen transport characteristics in amorphous materials without long-range crystal order.

Negative Differential Resistance in Conical Nanopore Iontronic Memristors

Emerging ion transport dynamics with memory effects at nanoscale solution-substrate interfaces offers a unique opportunity to overcome the bottlenecks in traditional computational architectures, trade-offs in selectivity and throughput in separation, and electrochemical energy conversions. Negative differential resistance (NDR), a decrease in conductance with increasing potential, constitutes a new function from the perspective of time-dependent instead of steady-state nanoscale electrokinetic ion transport but remains unexplored in ionotronics to develop higher-order complexity and advanced capabilities. Herein, NDR is introduced in hysteretic and rectified ion transport through single conical nanopipettes (NPs) as ionic memristors. Deterministic and chaotic behaviors are controlled via an electric field as the sole stimulus. The NDR arises fundamentally from the availability and redistribution of the ionic charges during the hysteretic and rectified transport at asymmetric nanointerfaces. The elucidated mechanism is generalizable, and the drastically simplified operations enable tunable state-switching dynamics with higher-order complexity besides the first-order synaptic functions in multiple excitatory and inhibitory states.

Hysteresis in memristors produces conduction inductance and conduction capacitance effects

Memristors are devices in which the conductance state can be alternately switched between a high and a low value by means of a voltage scan. In general, systems involving a chemical inductor mechanism as solar cells, asymmetric nanopores in electrochemical cells, transistors, and solid state memristive devices, exhibit a current increase and decrease over time that generates hysteresis. By performing small signal ac impedance spectroscopy, we show that memristors, or any other system with hysteresis relying on the conductance modulation effect, display intrinsic dynamic inductor-like and capacitance-like behaviours in specific input voltage ranges. Both the conduction inductance and the conduction capacitance originate in the same delayed conduction process linked to the memristor dynamics and not in electromagnetic or polarization effects. A simple memristor model reproduces the main features of the transition from capacitive to inductive impedance spectroscopy spectra, which causes a nonzero crossing of current-voltage curves.

Solution-Processable and Printable Two-Dimensional Transition Metal Dichalcogenide Inks

Two-dimensional (2D) transition metal dichalcogenides (TMDs) with layered crystal structures have been attracting enormous research interest for their atomic thickness, mechanical flexibility, and excellent electronic/optoelectronic properties for applications in diverse technological areas. Solution-processable 2D TMD inks are promising for large-scale production of functional thin films at an affordable cost, using high-throughput solution-based processing techniques such as printing and roll-to-roll fabrications. This paper provides a comprehensive review of the chemical synthesis of solution-processable and printable 2D TMD ink materials and the subsequent assembly into thin films for diverse applications. We start with the chemical principles and protocols of various synthesis methods for 2D TMD nanosheet crystals in the solution phase. The solution-based techniques for depositing ink materials into solid-state thin films are discussed. Then, we review the applications of these solution-processable thin films in diverse technological areas including electronics, optoelectronics, and others. To conclude, a summary of the key scientific/technical challenges and future research opportunities of solution-processable TMD inks is provided.

Applying the Wake-Up-like Effect to Enhance the Capabilities of Two-Dimensional Ferroelectric Field-Effect Transistors

For traditional ferroelectric field-effect transistors (FeFETs), enhancing the polarization domain of bulk ferroelectric materials is essential to improve device performance. However, there has been limited investigation into the enhancement of polarization field in two-dimensional (2D) ferroelectric material such as CuInP2S6 (CIPS). In this study, similar to bulk ferroelectric materials, CIPS exhibited enhanced polarization field upon application of external cyclic voltage. Moreover, unlike traditional ferroelectric materials, the polarization enhancement of CIPS is not due to redistribution of the defect but rather originates from a mechanism: the long-distance migration of Cu ions. We termed this mechanism the "wake-up-like effect". After incorporating the wake-up-like effect into the graphene/CIPS/WSe2 FeFET device, we successfully increased the hysteresis window and enhanced the current on/off ratio by 4 orders of magnitude. Moreover, the FeFET yielded remarkable achievements, such as multilevel nonvolatile memory with 21 distinct conductance levels, a high on/off ratio exceeding 106, a long retention time exceeding 103 s, and neuromorphic computing with 93% accuracy at recognizing handwritten digits. Introducing the wake-up-like effect to 2D CIPS may pave the way for innovative approaches to achieve advanced multilevel nonvolatile memory and neuromorphic computing capabilities for next-generation micro-nanoelectronic devices.

Low Power Volatile and Nonvolatile Memristive Devices from 1D MoO2-MoS2 Core-Shell Heterostructures for Future Bio-Inspired Computing

Memristors-based integrated circuits for emerging bio-inspired computing paradigms require an integrated approach utilizing both volatile and nonvolatile memristive devices. Here, an innovative architecture comprising of 1D CVD-grown core-shell heterostructures (CSHSs) of MoO2-MoS2 is employed as memristors manifesting both volatile switching (with high selectivity of 107 and steep slope of 0.6 mV decade-1) and nonvolatile switching phenomena (with Ion/Ioff ≈103 and switching speed of 60 ns). In these CSHSs, the metallic core MoO2 with high current carrying capacity provides a conformal and immaculate interface with semiconducting MoS2 shells and therefore it acts as a bottom electrode for the memristors. The power consumption in volatile devices is as low as 50 pW per set transition and 0.1 fW in standby mode. Voltage-driven current spikes are observed for volatile devices while with nonvolatile memristors, key features of a biological synapse such as short/long-term plasticity and paired pulse facilitation are emulated suggesting their potential for the development of neuromorphic circuits. These CSHSs offer an unprecedented solution for the interfacial issues between metallic electrodes and the layered materials-based switching element with the prospects of developing smaller footprint memristive devices for future integrated circuits.

Oxovanadium electronics for in-memory, neuromorphic, and quantum computing applications

Vanadium is a critical raw material. In the nearby future, it may, however, become one of the key elements of computer devices based on two-dimensional arrays of spin qubits for quantum information processing or charge- and resistance-based data memory cells for non-volatile in-memory and neuromorphic computing. The research and development (R&D) of vanadium-containing electronic materials and methods for their responsible fabrication underpins the transition to innovative hybrid semiconductors for energy- and resource-efficient memory and information processing technologies. The combination of standard and emerging solid-state semiconductors with stimuli-responsive oxo complexes of vanadium(IV,V) is envisioned to result in electronics with a new room-temperature device nanophysics, and the ability to modulate and control it at the sub-nanometer level. The development of exponential (Boolean) logics based on the oxovanadium-comprising circuitry and crossbar arrays of individual memristive cells for in-memory computing, the implementation of basic synaptic functions via dynamic electrical pulses for neuromorphic computing, and the readout and control of spin networks and interfaces for quantum computing are strategically important future areas of molecular chemistry and applied physics of vanadium.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device's superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

Resistive Memory Devices at the Thinnest Limit: Progress and Challenges

The Si-based integrated circuits industry has been developing for more than half a century, by focusing on the scaling-down of transistor. However, the miniaturization of transistors will soon reach its physical limits, thereby requiring novel material and device technologies. Resistive memory is a promising candidate for in-memory computing and energy-efficient synaptic devices that can satisfy the computational demands of the future applications. However, poor cycle-to-cycle and device-to-device uniformities hinder its mass production. 2D materials, as a new type of semiconductor, is successfully employed in various micro/nanoelectronic devices and have the potential to drive future innovation in resistive memory technology. This review evaluates the potential of using the thinnest advanced materials, that is, monolayer 2D materials, for memristor or memtransistor applications, including resistive switching behavior and atomic mechanism, high-frequency device performances, and in-memory computing/neuromorphic computing applications. The scaling-down advantages of promising monolayer 2D materials including graphene, transition metal dichalcogenides, and hexagonal boron nitride are presented. Finally, the technical challenges of these atomic devices for practical applications are elaborately discussed. The study of monolayer-2D-material-based resistive memory is expected to play a positive role in the exploration of beyond-Si electronic technologies.

Realization of sextuple polarization states and interstate switching in antiferroelectric CuInP2S6

Realization of higher-order multistates with mutual interstate switching in ferroelectric materials is a perpetual drive for high-density storage devices and beyond-Moore technologies. Here we demonstrate experimentally that antiferroelectric van der Waals CuInP2S6 films can be controllably stabilized into double, quadruple, and sextuple polarization states, and a system harboring polarization order of six is also reversibly tunable into order of four or two. Furthermore, for a given polarization order, mutual interstate switching can be achieved via moderate electric field modulation. First-principles studies of CuInP2S6 multilayers help to reveal that the double, quadruple, and sextuple states are attributable to the existence of respective single, double, and triple ferroelectric domains with antiferroelectric interdomain coupling and Cu ion migration. These findings offer appealing platforms for developing multistate ferroelectric devices, while the underlining mechanism is transformative to other non-volatile material systems.

Multi-channel broadband nonvolatile programmable modal switch

Mode-division multiplexing (MDM) in chip-scale photonics is paramount to sustain data capacity growth and reduce power consumption. However, its scalability hinges on developing efficient and dynamic modal switches. Existing active modal switches suffer from substantial static power consumption, large footprints, and narrow bandwidth. Here, we present, for the first time, to the best of our knowledge, a novel multiport, broadband, non-volatile, and programmable modal switch designed for on-chip MDM systems. Our design leverages the unique properties of integrating nanoscale phase-change materials (PCM) within a silicon photonic architecture. This enables independent manipulation of spatial modes, allowing for dynamic, non-volatile, and selective routing to six distinct output ports. Crucially, our switch outperforms current dynamic modal switches by offering non-volatile, energy-efficient multiport functionality and excels in performance metrics. Our switch exhibits exceptional broadband operating bandwidth exceeding 70 nm, with low loss (< 1 dB), and a high extinction ratio (> 10 dB). Our framework provides a step forward in chip-scale MDM, paving the way for future green and scalable data centers and high-performance computers.

Generation and Storage of Random Voltage Values via Ring Oscillators Comprising Feedback Field-Effect Transistors

In this study, we demonstrate the generation and storage of random voltage values using a ring oscillator consisting of feedback field-effect transistors (FBFETs). This innovative approach utilizes the logic-in-memory function of FBFETs to extract continuous output voltages from oscillatory cycles. The ring oscillator exhibited uniform probability distributions of 51.6% for logic 0 and 48.4% for logic 1. The generation of analog voltages provides binary random variables that are stored for over 5000 s. This demonstrates the potential of the ring oscillator in advanced physical functions and true random number generator technologies.

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays

Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaOx/HfOx/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.

Ag-doped non-imperfection-enabled uniform memristive neuromorphic device based on van der Waals indium phosphorus sulfide

Memristors are considered promising energy-efficient artificial intelligence hardware, which can eliminate the von Neumann bottleneck by parallel in-memory computing. The common imperfection-enabled memristors are plagued with critical variability issues impeding their commercialization. Reported approaches to reduce the variability usually sacrifice other performances, e.g., small on/off ratios and high operation currents. Here, we demonstrate an unconventional Ag-doped nonimperfection diffusion channel-enabled memristor in van der Waals indium phosphorus sulfide, which can combine ultralow variabilities with desirable metrics. We achieve operation voltage, resistance, and on/off ratio variations down to 3.8, 2.3, and 6.9% at their extreme values of 0.2 V, 1011 ohms, and 108, respectively. Meanwhile, the operation current can be pushed from 1 nA to 1 pA at the scalability limit of 6 nm after Ag doping. Fourteen Boolean logic functions and convolutional image processing are successfully implemented by the memristors, manifesting the potential for logic-in-memory devices and efficient non-von Neumann accelerators.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors

Resistive switching devices based on the Au/Ti/TiO2/Au stack were developed. In addition to standard electrical characterization by means of I-V curves, scanning thermal microscopy was employed to localize the hot spots on the top device surface (linked to conductive nanofilaments, CNFs) and perform in-operando tracking of temperature in such spots. In this way, electrical and thermal responses can be simultaneously recorded and related to each other. In a complementary way, a model for device simulation (based on COMSOL Multiphysics) was implemented in order to link the measured temperature to simulated device temperature maps. The data obtained were employed to calculate the thermal resistance to be used in compact models, such as the Stanford model, for circuit simulation. The thermal resistance extraction technique presented in this work is based on electrical and thermal measurements instead of being indirectly supported by a single fitting of the electrical response (using just I-V curves), as usual. Besides, the set and reset voltages were calculated from the complete I-V curve resistive switching series through different automatic numerical methods to assess the device variability. The series resistance was also obtained from experimental measurements, whose value is also incorporated into a compact model enhanced version.

Electrochemical rewiring through quantum conductance effects in single metallic memristive nanowires

Memristive devices have been demonstrated to exhibit quantum conductance effects at room temperature. In these devices, a detailed understanding of the relationship between electrochemical processes and ionic dynamic underlying the formation of atomic-sized conductive filaments and corresponding electronic transport properties in the quantum regime still represents a challenge. In this work, we report on quantum conductance effects in single memristive Ag nanowires (NWs) through a combined experimental and simulation approach that combines advanced classical molecular dynamics (MD) algorithms and quantum transport simulations (DFT). This approach provides new insights on quantum conductance effects in memristive devices by unravelling the intrinsic relationship between electronic transport and atomic dynamic reconfiguration of the nanofilment, by shedding light on deviations from integer multiples of the fundamental quantum of conductance depending on peculiar dynamic trajectories of nanofilament reconfiguration and on conductance fluctuations relying on atomic rearrangement due to thermal fluctuations.

Heterogeneous reservoir computing in second-order Ta2O5/HfO2 memristors

Multiple switching modes in a Ta2O5/HfO2 memristor are studied experimentally and numerically through a reservoir computing (RC) simulation to reveal the importance of nonlinearity and heterogeneity in the RC framework. Unlike most studies, where homogeneous reservoirs are used, heterogeneity is introduced by combining different behaviors of the memristor units. The chosen memristor for the reservoir units is based on a Ta2O5/HfO2 bilayer, in which the conductances of the Ta2O5 and HfO2 layers are controlled by the oxygen vacancies and deep/shallow traps, respectively, providing both volatile and non-volatile resistive switching modes. These several control parameters make the second-order Ta2O5/HfO2 memristor system present different behaviors in agreement with its history-dependent conductance and allow the fine-tuning of the behavior of each reservoir unit. The heterogeneity in the reservoir units improves the pattern recognition performance in the heterogeneous memristor RC system with a similar physical structure.

3D simulation of conductive nanofilaments in multilayer h-BN memristors via a circuit breaker approach

A 3D simulation of conductive nanofilaments (CNFs) in multilayer hexagonal-BN memristors is performed. To do so, a simulation tool based on circuit breakers is developed including for the first time a 3D resistive network. The circuit breakers employed can be modeled with two, three and four resistance states; in addition, a series resistance and a module to account for quantum effects, by means of the quantum point contact model, are also included. Finally, to describe real dielectric situations, regions with a high defect density are modeled with a great variety of geometrical shapes to consider their influence in the resistive switching (RS) process. The simulator has been tuned with measurements of h-BN memristive devices, fabricated with chemical-vapour-deposition grown h-BN layers, which were electrically and physically characterized. We show the formation of CNFs that produce filamentary charge conduction in our devices. Moreover, the simulation tool is employed to describe partial filament rupture in reset processes and show the low dependence of the set voltage on the device area, which is seen experimentally.

Opto-Electrochemical Synaptic Memory in Supramolecularly Engineered Janus 2D MoS2

Artificial synapses combining multiple yet independent signal processing strategies in a single device are key enabler to achieve high-density of integration, energy efficiency, and fast data manipulation in brain-like computing. By taming functional complexity, the use of hybrids comprising multiple materials as active components in synaptic devices represents a powerful route to encode both short-term potentiation (STP) and long-term potentiation (LTP) in synaptic circuitries. To meet such a grand challenge, herein a novel Janus 2D material is developed by dressing asymmetrically the two surfaces of 2D molybdenum disulfide (MoS2 ) with an electrochemically-switchable ferrocene (Fc)/ ferrocenium (Fc+ ) redox couple and an optically-responsive photochromic azobenzene (Azo). Upon varying the magnitude of the electrochemical stimulus, it is possible to steer the transition between STP and LTP, thereby either triggering electrochemical doping of Fc/Fc+  pair on MoS2  or controlling an adsorption/desorption process of such redox species on MoS2 . In addition, a lower magnitude LTP is recorded by activating the photoisomerization of azobenzene chemisorbed molecules and therefore modulating the dipole-induced doping of the 2D semiconductor. Significantly, the interplay of electrochemical and optical stimuli makes it possible to construct artificial synapses where LTP can be boosted to 4-bit (16 memory states) while simultaneously functioning as STP.

Ni Single-Atoms Based Memristors with Ultrafast Speed and Ultralong Data Retention

Memristor with low-power, high density, and scalability fulfills the requirements of the applications of the new computing system beyond Moore's law. However, there are still nonideal device characteristics observed in the memristor to be solved. The important observation is that retention and speed are correlated parameters of memristor with trade off against each other. The delicately modulating distribution and trapping level of defects in electron migration-based memristor is expected to provide a compromise method to address the contradictory issue of improving both switching speed and retention capability. Here, high-performance memristor based on the structure of ITO/Ni single-atoms (NiSAs/N-C)/Polyvinyl pyrrolidone (PVP)/Au is reported. By utilizing well-distributed trapping sites , small tunneling barriers/distance and high charging energy, the memristor with an ultrafast switching speed of 100 ns, ultralong retention capability of 106  s, a low set voltage (Vset ) of ≈0.7 V, a substantial ON/OFF ration of 103 , and low spatial variation in cycle-to-cycle (500 cycles) and device-to-device characteristics (128 devices) is demonstrated. On the premise of preserving the strengths of a fast switching speed, this memristor exhibits ultralong retention capability comparable to the commercialized flash memory. Finally, a memristor ratioed logic-based combinational memristor array to realize the one-bit full adder is further implemented.

Memristive Response and Capacitive Spiking in Aqueous Ion Transport through Two-Dimensional Nanopore Arrays

In living organisms, information is processed in interconnected symphonies of ionic currents spiking through protein ion channels. As a result of dynamic switching of their conductive states, ion channels exhibit a variety of current-voltage nonlinearities and memory effects. Fueled by the promise of computing architectures entirely different from von Neumann, recent attempts to identify and harness similar phenomena in artificial nanofluidic environments focused on demonstrating analogue circuit elements with memory. Here we explore aqueous ionic transport through two-dimensional (2D) membranes featuring arrays of ion-trapping crown-ether-like pores. We demonstrate that for aqueous salts featuring ions with different ion-pore binding affinities, memristive effects emerge through coupling between the time-delayed state of the system and its transport properties. We also demonstrate a nanopore array that behaves as a capacitor with a strain-tunable built-in barrier, yielding behaviors ranging from current spiking to an ohmic response. By focusing on the illustrative underlying mechanisms, we demonstrate that realistically observable memory effects may be achieved in nanofluidic systems featuring crown-porous 2D membranes.

Magnetic-ferroelectric synergic control of multilevel conducting states in van der Waals multiferroic tunnel junctions towards in-memory computing

van der Waals (vdW) multiferroic tunnel junctions (MFTJs) based on two-dimensional materials have gained significant interest due to their potential applications in next-generation data storage and in-memory computing devices. In this study, we construct vdW MFTJs by employing monolayer Mn2Se3 as the spin-filter tunnel barrier, TiTe2 as the electrodes and In2S3 as the tunnel barrier to investigate the spin transport properties based on first-principles quantum transport calculations. It is highlighted that apparent tunneling magnetoresistance (TMR) and tunneling electroresistance (TER) effects with a maximum TMR ratio of 6237% and TER ratio of 1771% can be realized by using bilayer In2S3 as the tunnel barrier under finite bias. Furthermore, the physical origin of the distinguished TMR and TER effects is unraveled from the k||-resolved transmission spectra and spin-dependent projected local density of states analysis. Interestingly, four distinguishable conductance states reveal the implementation of four-state nonvolatile data storage using one MFTJ unit. More importantly, in-memory logic computing and multilevel data storage can be achieved at the same time by magnetic switching and electrical control, respectively. These results shed light on vdW MFTJs in the applications of in-memory computing as well as multilevel data storage devices.

Physically Unclonable Holographic Encryption and Anticounterfeiting Based on the Light Propagation of Complex Medium and Fluorescent Labels

Physically unclonable function (PUF) methods have high security, but their wide application is limited by complex encoding, large database, advanced external characterization equipment, and complicated comparative authentication. Therefore, we creatively propose the physically unclonable holographic encryption and anticounterfeiting based on the light propagation of complex medium and fluorescent labels. As far as we know, this is the first holographic encryption and anticounterfeiting method with a fluorescence physically unclonable property. The proposed method reduces the above requirements of traditional PUF methods and significantly reduces the cost. The angle-multiplexed PUF fluorescent label is the physical secret key. The information is encrypted as computer-generated holograms (CGH). Many physical parameters in the system are used as the parameter secret keys. The Diffie-Hellman key exchange algorithm is improved to transfer parameter secret keys. A variety of complex medium hologram generation methods are proposed and compared. The effectiveness, security, and robustness of the method are studied and analyzed. Finally, a graphical user interface (GUI) is designed for the convenience of users. The advantages of this method include lower PUF encoding complexity, effective reduction of the database size, lower requirements for characterization equipment, and direct use of decrypted information without complicated comparative authentication to reduce misjudgment. It is believed that the method proposed in this paper will pave the way for the popularization and application of PUF-based anticounterfeiting and encryption methods.

A Star Network of Bipolar Memristive Devices Enables Sensing and Temporal Computing

Temporal (race) computing schemes rely on temporal memories, where information is represented with the timing of signal edges. Standard digital circuit techniques can be used to capture the relative timing characteristics of signal edges. However, the properties of emerging device technologies could be particularly exploited for more efficient circuit implementations. Specifically, the collective dynamics of networks of memristive devices could be leveraged to facilitate time-domain computations in emerging memristive memories. To this end, this work studies the star interconnect configuration of bipolar memristive devices. Through circuit simulations using a behavioral model of voltage-controlled bipolar memristive devices, we demonstrated the suitability of such circuits in two different contexts, namely sensing and "rank-order" coding. We particularly analyzed the conditions that the employed memristive devices should meet to guarantee the expected operation of the circuit and the possible effects of device variability in the storage and the reproduction of the information in arriving signal edges. The simulation results in LTSpice validate the correct operation and confirm the promising application prospects of such simple circuit structures, which, we show, natively exist in the crossbar geometry. Therefore, the star interconnect configuration could be considered for temporal computations inside resistive memory (ReRAM) arrays.

A ferroelectric fin diode for robust non-volatile memory

Among today's nonvolatile memories, ferroelectric-based capacitors, tunnel junctions and field-effect transistors (FET) are already industrially integrated and/or intensively investigated to improve their performances. Concurrently, because of the tremendous development of artificial intelligence and big-data issues, there is an urgent need to realize high-density crossbar arrays, a prerequisite for the future of memories and emerging computing algorithms. Here, a two-terminal ferroelectric fin diode (FFD) in which a ferroelectric capacitor and a fin-like semiconductor channel are combined to share both top and bottom electrodes is designed. Such a device not only shows both digital and analog memory functionalities but is also robust and universal as it works using two very different ferroelectric materials. When compared to all current nonvolatile memories, it cumulatively demonstrates an endurance up to 1010 cycles, an ON/OFF ratio of ~102, a feature size of 30 nm, an operating energy of ~20 fJ and an operation speed of 100 ns. Beyond these superior performances, the simple two-terminal structure and their self-rectifying ratio of ~ 104 permit to consider them as new electronic building blocks for designing passive crossbar arrays which are crucial for the future in-memory computing.

Solution-Processed Robust Multifunctional Memristor of 2D Layered Material Thin Film

Memristors have gained significant attention recently due to their unique ability to exhibit functionalities for brain-inspired neuromorphic computing. Here, we demonstrate a high-performance multifunctional memristor using a thin film of liquid-phase exfoliated (LPE) 2D MoS2 pinched between two electrodes. Nanoscale inspection of a solution-processed MoS2 thin film using scanning electron and scanning probe microscopies revealed the high-quality and defect-free nature. Systematic current-voltage (I-V) characterizations depict a facile, nonvolatile resistive switching behavior of our 2D MoS2 thin film device with a current On/Off ratio of 103 and energy cost of only a few picojoules. Excellent performance metrics, including at least 103 cycle endurance, 104 s retention, and switching speed down to a few nanoseconds, reflect robust high-performance data storage capability. Charge carriers trapping and detrapping at the sulfur vacancy defect sites in MoS2 nanosheets mainly display the resistive switching property, supported by the impedance analysis and theoretical fitting results. Multifunctionality is leveraged through implementing two-input logic gate operations, edge computation, and crucial adaptive learning via a Pavlov's dogs experiment. Overall, our solution-processed MoS2 memristor has the potential for tremendous future opportunities in integrated circuits and different computing paradigms, including energy-efficient neuromorphic computing hardware in artificial intelligence.

Multistate structures in a hydrogen-bonded polycatenation non-covalent organic framework with diverse resistive switching behaviors

The inherent structural flexibility and reversibility of non-covalent organic frameworks have enabled them to exhibit switchable multistate structures under external stimuli, providing great potential in the field of resistive switching (RS), but not well explored yet. Herein, we report the 0D+1D hydrogen-bonded polycatenation non-covalent organic framework (HOF-FJU-52), exhibiting diverse and reversible RS behaviors with the high performance. Triggered by the external stimulus of electrical field E at room temperature, HOF-FJU-52 has excellent resistive random-access memory (RRAM) behaviors, comparable to the state-of-the-art materials. When cooling down below 200 K, it was transferred to write-once-read-many-times memory (WORM) behaviors. The two memory behaviors exhibit reversibility on a single crystal device through the temperature changes. The RS mechanism of this non-covalent organic framework has been deciphered at the atomic level by the detailed single-crystal X-ray diffraction analyses, demonstrating that the structural dual-flexibility both in the asymmetric hydrogen bonded dimers within the 0D loops and in the infinite π-π stacking column between the loops and chains contribute to reversible structure transformations between multi-states and thus to its dual RS behaviors.

Ultralow Energy Consumption Angstrom-Fluidic Memristor

The emergence of nanofluidic memristors has made a giant leap to mimic the neuromorphic functions of biological neurons. Here, we report neuromorphic signaling using Angstrom-scale funnel-shaped channels with poly-l-lysine (PLL) assembled at nano-openings. We found frequency-dependent current-voltage characteristics under sweeping voltage, which represents a diode in low frequencies, but it showed pinched current hysteresis as frequency increases. The current hysteresis is strongly dependent on pH values but weakly dependent on salt concentration. We attributed the current hysteresis to the entropy barrier of PLL molecules entering and exiting the Angstrom channels, resulting in reversible voltage-gated open-close state transitions. We successfully emulated the synaptic adaptation of Hebbian learning using voltage spikes and obtained a minimum energy consumption of 2-23 fJ in each spike per channel. Our findings pave a new way to mimic neuronal functions by Angstrom channels in low energy consumption.

Resistive switching modulation by incorporating thermally enhanced layer in HfO2-based memristor

Oxide-based memristors by incorporating thermally enhanced layer (TEL) have showed great potential in electronic devices for high-efficient and high-density neuromorphic computing owing to the improvement of multilevel resistive switching. However, research on the mechanism of resistive switching regulation is still lacking. In this work, based on the method of finite element numerical simulation analysis, a bilayer oxide-based memristor Pt/HfO2(5 nm)/Ta2O5(5 nm)/Pt with the Ta2O5TEL was proposed. The oxygen vacancy concentrates distribution shows that the fracture of conductive filaments (CF) is at the interface where the local temperature is the highest during the reset process. The multilevel resistive switching properties were also obtained by applying different stop voltages. The fracture gap of CF can be enlarged with the increase of the stopping voltage, which is attributed to the heat-gathering ability of the TEL. Moreover, it was found that the fracture position of oxygen CF is dependent on the thickness of TEL, which exhibits a modulation of device RS performance. These results provide a theoretical guidance on the suitability of memristor devices for use in high-density memory and brain-actuated computer systems.

Effect of electrode materials on resistive switching behaviour of NbOx-based memristive devices

Memristive devices that rely on redox-based resistive switching mechanism have attracted great attention for the development of next-generation memory and computing architectures. However, a detailed understanding of the relationship between involved materials, interfaces, and device functionalities still represents a challenge. In this work, we analyse the effect of electrode metals on resistive switching functionalities of NbOx-based memristive cells. For this purpose, the effect of Au, Pt, Ir, TiN, and Nb top electrodes was investigated in devices based on amorphous NbOx grown by anodic oxidation on a Nb substrate exploited also as counter electrode. It is shown that the choice of the metal electrode regulates electronic transport properties of metal-insulator interfaces, strongly influences the electroforming process, and the following resistive switching characteristics. Results show that the electronic blocking character of Schottky interfaces provided by Au and Pt metal electrodes results in better resistive switching performances. It is shown that Pt represents the best choice for the realization of memristive cells when the NbOx thickness is reduced, making possible the realization of memristive cells characterised by low variability in operating voltages, resistance states and with low device-to-device variability. These results can provide new insights towards a rational design of redox-based memristive cells.

In-situ electro-responsive through-space coupling enabling foldamers as volatile memory elements

Voltage-gated processing units are fundamental components for non-von Neumann architectures like memristor and electric synapses, on which nanoscale molecular electronics have possessed great potentials. Here, tailored foldamers with furan‒benzene stacking (f-Fu) and thiophene‒benzene stacking (f-Th) are designed to decipher electro-responsive through-space interaction, which achieve volatile memory behaviors via quantum interference switching in single-molecule junctions. f-Fu exhibits volatile turn-on feature while f-Th performs stochastic turn-off feature with low voltages as 0.2 V. The weakened orbital through-space mixing induced by electro-polarization dominates stacking malposition and quantum interference switching. f-Fu possesses higher switching probability and faster responsive time, while f-Th suffers incomplete switching and longer responsive time. High switching ratios of up to 91 for f-Fu is realized by electrochemical gating. These findings provide evidence and interpretation of the electro-responsiveness of non-covalent interaction at single-molecule level and offer design strategies of molecular non-von Neumann architectures like true random number generator.

A High-Entropy-Oxides-Based Memristor: Outstanding Resistive Switching Performance and Mechanisms in Atomic Structural Evolution

The application of high-entropy oxide (HEO) has attracted significant attention in recent years owing to their unique structural characteristics, such as excellent electrochemical properties and long-term cycling stability. However, the application of resistive random-access memory (RRAM) has not been extensively studied, and the switching mechanism of HEO-based RRAM has yet to be thoroughly investigated. In this study, HEO (Cr, Mn, Fe, Co, Ni)3 O4 with a spinel structure is epitaxially grown on a Nb:STO conductive substrate, and Pt metal is deposited as the top electrode. After the resistive-switching operation, some regions of the spinel structure are transformed into a rock-salt structure and analyzed using advanced transmission electron microscopy and scanning transmission electron microscopy. From the results of X-ray photoelectron spectroscopy and electron energy loss spectroscopy, only specific elements would change their valence state, which results in excellent resistive-switching properties with a high on/off ratio on the order of 105 , outstanding endurance (>4550 cycles), long retention time (>104 s), and high stability, which suggests that HEO is a promising RRAM material.

TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance

We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics.

Edge learning using a fully integrated neuro-inspired memristor chip

Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.