Thousands of conductance levels in memristors integrated on CMOS

Data encryption/decryption and medical image reconstruction based on a sustainable biomemristor designed logic gate circuit

Memristors are considered one of the most promising new-generation memory technologies due to their high integration density, fast read/write speeds, and ultra-low power consumption. Natural biomaterials have attracted interest in integrated circuits and electronics because of their environmental friendliness, sustainability, low cost, and excellent biocompatibility. In this study, a sustainable biomemristor with Ag/mugwort:PVDF/ITO structure was prepared using spin-coating and magnetron sputtering methods, which exhibited excellent durability, significant resistance switching (RS) behavior and unidirectional conduction properties when three metals were used as top electrode. By studying the conductivity mechanism of the device, a charge conduction model was established by the combination of F-N tunneling, redox, and complexation reaction. Finally, the novel logic gate circuits were constructed using the as-prepared memristor, and further memristor based encryption circuit using 3-8 decoder was innovatively designed, which can realize uniform rule encryption and decryption of medical information for data and medical images. Therefore, this work realizes the integration of memristor with traditional electronic technology and expands the applications of sustainable biomemristors in digital circuits, data encryption, and medical image security.

Parylene-MoOx crossbar memristors as a volatile reservoir and non-volatile readout: a homogeneous reservoir computing system

From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system. However, these parts require distinct behaviors found in differing memristive structures. The development of a homogeneous memristive reservoir computing system will significantly facilitate and reduce the fabrication process cost. The following work employs the co-existence of volatile and non-volatile regimes in parylene-MoOx crossbar memristors controlled by compliance current for this aim. The stable operation of the memristors under study is confirmed by low cycle-to-cycle and device-to-device variations of the switching voltages. For the transition between the volatile and non-volatile regimes, factors such as compliance current and reading voltage along with possible intrinsic origins are discussed. The results provide a foundation for the future hardware development of a homogeneous parylene-based reservoir computing system, considering high MNIST dataset classification accuracy (∼96%).

Kinetic Monte Carlo simulation analysis of the conductance drift in Multilevel HfO2-based RRAM devices

The drift characteristics of valence change memory (VCM) devices have been analyzed through both experimental analysis and 3D kinetic Monte Carlo (kMC) simulations. By simulating six distinct low-resistance states (LRS) over a 24-hour period at room temperature, we aim to assess the device temporal stability and retention. Our results demonstrate the feasibility of multi-level operation and reveal insights into the conductive filament (CF) dynamics. The cumulative distribution functions (CDFs) of read-out currents measured at different time intervals provide a comprehensive view of the device performance for the different conductance levels. These findings not only enhance the understanding of VCM device switching behaviour but also allow the development of strategies for improving retention, thereby advancing the development of reliable nonvolatile resistive switching memory technologies.

Reliable and Robust Two-Dimensional Perovskite Memristors for Flexible-Resistive Random-Access Memory Array

Two-dimensional (2D) halide perovskites have become a promising class of memristive materials due to their low power consumption, compositional versatility, and microstructural anisotropy in electronics. However, implementing high-performance resistive random-access memory requires a higher reliability and moisture resistance. To address these issues, component studies and attempts to improve the phase stability have been reported but have not been able to achieve sufficient reliability. Here, highly textured thin films grown perpendicular to the substrate in Ruddlesden-Popper 2D perovskites exhibited highly stable and reliable binary memory performance. We further built a flexible crossbar array to verify data storage capability, achieving a high device yield, robust endurance, long retention, reliability to operate under bending conditions, and moisture stability over a year. These device performances are attributed to preformed vertically oriented nanocrystals that allow the conductive filaments to operate reliably. Our finding provides the material design strategy that can be extended to the development of semiconductor materials for next-generation memory devices.

Hyperplane tree-based data mining with a multi-functional memristive crossbar array

This study explores the stochastic and binary switching behaviors of a Ta/HfO2/RuO2 memristor to implement a combined data mining approach for outlier detection and data clustering algorithms in a multi-functional memristive crossbar array. The memristor switches stochastically with high state dispersion in the stochastic mode and deterministically between two states with low dispersion in the binary mode, while they can be controlled by varying operating voltages. The stochastic mode facilitates the parallel generation of random hyperplanes in a tree structure, used to compress spatial information of the dataset in the Euclidian space into binary format, still retaining sufficient spatial features. The ensemble effect from multiple trees improved the classification performance. The binary mode facilitates parallel Hamming distance calculation of the binary codes containing spatial information, which measures similarity. These two modes enable efficient implementation of the newly proposed minority-based outlier detection method and modified K-means method on the same hardware. Array measurements and hardware simulations investigate various hyperparameters' impact and validate the proposed methods with practical datasets. The proposed methods show linear O(n) time complexity and high energy efficiency, consuming <1% of the energy compared to digital computing with conventional algorithms while demonstrating software-comparable performance in both tasks.

Constructing a supercapacitor-memristor through non-linear ion transport in MOF nanochannels

The coexistence and coupling of capacitive and memristive effects have been an important subject of scientific interest. While the capacitive effect in memristors has been extensively studied, the reciprocal scenario of the memristive effect in capacitors remains unexplored. In this study, we introduce a supercapacitor-memristor (CAPistor) concept, which is constructed by leveraging non-linear ion transport within the pores of a metal-organic framework zeolitic-imidazolate framework (ZIF-7). Within the nanochannels of the ZIF-7 electrode in an aqueous pseudocapacitor, the anionic species (OH-) of the electrolyte can be enriched and dissipated in different voltage regimes. This difference leads to a hysteresis effect in ion conductivity, constituting a memristive behavior in the pseudocapacitor. Thus, the pseudocapacitor-converted CAPistor seamlessly integrates the programmable resistance and memory functions of an ionic memristor into a supercapacitor, demonstrating enormous potential to extend the traditional energy storage applications of supercapacitors into emerging fields, including biomimetic nanofluidic ionics and neuromorphic computing.

Reliable Low-Current and Multilevel Memristive Electrochemical Neuromorphic Devices with Semi-Metal Sb Filament

Memristors are used in artificial neural networks owing to their exceptional integration capabilities and scalability. However, traditional memristors are hampered by limited resistance states and randomness, which curtails their application. The migration of metal ions critically influences the number of conductance states and the linearity of weight updates. Semi-metal filaments can provide subquantum conductance changes to the memristors due to the smaller single-atom conductance, such as Sb (≈0.01 G0 = 7.69 × 10-7 S). Here, a memristor featuring an active electrode composed of semi-metal Sb is introduced for the first time. This memristor demonstrates precise conductance control, a large on/off ratio, consistent switching, and prolonged retention exceeding 105 s. Density functional theory (DFT) calculations and characterization methods reveal the formation of Sb filaments during a set process. The interaction between Sb and O within the dielectric layer facilitates the Sb filaments' ability to preserve their morphology in the absence of electric fields.

In-Materia Annealing and Combinatorial Optimization Based on Vertical Memristive Array

Due to its area and energy efficiency, a memristive crossbar array (CBA) has been extensively studied for various combinatorial optimization applications, from network problems to circuit design. However, conventional approaches include heavily burdening software fine-tuning for the annealing process. Instead, this study introduces the "in-materia annealing" method, where the inter-layer interference of vertically stacked memristive CBA is utilized as an annealing method. When mapping combinatorial optimization problems into the configuration layer of the CBA, exponentially decaying annealing profiles are generated in nearby noise layers. Moreover, in-materia annealing profiles can be controlled by changing compliance current, read voltage, and read pulse width. Therefore, the annealing profiles can be arbitrarily controlled and generated individually for each cell, providing rich noise sources to solve the problem efficiently. Consequently, the experimental and simulation of Max-Cut and weighted Max-Cut problems achieve notable results with the minimum software burden.

Polarization Pruning: Reliability Enhancement of Hafnia-Based Ferroelectric Devices for Memory and Neuromorphic Computing

Ferroelectric (FE) materials are key to advancing electronic devices owing to their non-volatile properties, rapid state-switching abilities, and low-energy consumption. FE-based devices are used in logic circuits, memory-storage devices, sensors, and in-memory computing. However, the primary challenge in advancing the practical applications of FE-based memory is its reliability. To address this problem, a novel polarization pruning (PP) method is proposed. The PP is designed to eliminate weakly polarized domains by applying an opposite-sign pulse immediately after a program or erase operation. Significant improvements in the reliability of ferroelectric devices are achieved by reducing the depolarization caused by weakly polarized domains and mitigating the fluctuations in the ferroelectric dipole. These enhancements include a 25% improvement in retention, a 50% reduction in read noise, a 45% decrease in threshold voltage variation, and a 72% improvement in linearity. The proposed PP method significantly improves the memory storage efficiency and performance of neuromorphic systems.

Adapting magnetoresistive memory devices for accurate and on-chip-training-free in-memory computing

Memristors have emerged as promising devices for enabling efficient multiply-accumulate (MAC) operations in crossbar arrays, crucial for analog in-memory computing (AiMC). However, variations in memristors and associated circuits can affect the accuracy of analog computing. Typically, this is mitigated by on-chip training, which is challenging for memristors with limited endurance. We present a hardware-software codesign using magnetic tunnel junction (MTJ)-based AiMC off-chip calibration that achieves software accuracy without costly on-chip training. Hardware-wise, MTJ devices exhibit ultralow cycle-to-cycle variations, as experimentally evaluated over 1 million mass-produced devices. Software-wise, leveraging this, we propose an off-chip training method to adjust deep neural network parameters, achieving accurate AiMC inference. We validate this approach with MAC operations, showing improved transfer curve linearity and reduced errors. By emulating large-scale neural network models, our codesigned MTJ-based AiMC closely matches software baseline accuracy and outperforms existing off-chip training methods, highlighting MTJ's potential in AI tasks.

Heterogeneous density-based clustering with a dual-functional memristive array

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Supramolecular Electronics: Monolayer Assembly of Nonamphiphilic Molecules via Water Surface-Assisted Molecular Deposition

Utilizing softly confined self-assembly at the water surface represents a promising approach for the fabrication of two-dimensional molecular monolayers (2D MMs), which have predominantly been concentrated on amphiphilic organic compounds before. Herein, we introduce a straightforward method termed "water surface-assisted molecular deposition (WSAMD)" to organize nonamphiphilic molecules into dense monolayers with high reproducibility. To underscore the versatility and merit of this methodology in the field of supramolecular electronics, we have successfully fabricated a range of defect-free, uniform semiconducting polymer monolayers, featuring a thickness reflective of molecular architectures. The charge carrier mobility could reach 0.05 cm2 V-1 s-1 for holes and 3.5 × 10-4 cm2 V-1 s-1 for electrons, respectively, in p-type and n-type polymeric monolayers when tested as the active layer in field-effect transistors. Furthermore, in situ polymerization reactions can be exploited to generate conductive monolayers of macromolecules such as polybenzylaniline (PBnANI) and polypyrrole (PPy), where PBnANI monolayers exhibit channel length-dependent conductivity, up to 0.37 S cm-1. The advent of the WSAMD method heralds a significant leap forward in the advancement of molecular 2D materials, catalyzing new avenues of exploration within material chemistry.

Toward a Brain-Neuromorphics Interface

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

Linear symmetric self-selecting 14-bit kinetic molecular memristors

Artificial Intelligence (AI) is the domain of large resource-intensive data centres that limit access to a small community of developers1,2. Neuromorphic hardware promises greatly improved space and energy efficiency for AI but is presently only capable of low-accuracy operations, such as inferencing in neural networks3-5. Core computing tasks of signal processing, neural network training and natural language processing demand far higher computing resolution, beyond that of individual neuromorphic circuit elements6-8. Here we introduce an analog molecular memristor based on a Ru-complex of an azo-aromatic ligand with 14-bit resolution. Precise kinetic control over a transition between two thermodynamically stable molecular electronic states facilitates 16,520 distinct analog conductance levels, which can be linearly and symmetrically updated or written individually in one time step, substantially simplifying the weight update procedure over existing neuromorphic platforms3. The circuit elements are unidirectional, facilitating a selector-less 64 × 64 crossbar-based dot-product engine that enables vector-matrix multiplication, including Fourier transform, in a single time step. We achieved more than 73 dB signal-to-noise-ratio, four orders of magnitude improvement over the state-of-the-art methods9-11, while consuming 460× less energy than digital computers12,13. Accelerators leveraging these molecular crossbars could transform neuromorphic computing, extending it beyond niche applications and augmenting the core of digital electronics from the cloud to the edge12,13.

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.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Dynamic memristor for physical reservoir computing

Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.

Electrical switching properties of Ag2S/Cu3P under light and heat excitation

In this paper, we prepared and investigated the electrical switching behaviors of Cu3P/Ag2S heterojunction in the absence/presence of light/heat excitation. The structure exhibited bipolar memristor characteristics. The resistive switching mechanism is due to the formation of Ag conductive filaments and phase transition in Cu3P. We found that the resistance ratio (ROFF/RON) increased by a factor of 1.4/1.8 after light/heat excitation. The underlying mechanism was due to the photoelectric effect/Seebeck effect. Our results are helpful for the understanding of the resistive switching performance of Cu3P/Ag2S junctions, providing valuable insights into the factors influencing resistive switching performance and a clue for the enhancement of the memristor performance.

Multidimensional vision sensors for information processing

The visual scene in the physical world integrates multidimensional information (spatial, temporal, polarization, spectrum and so on) and typically shows unstructured characteristics. Conventional image sensors cannot process this multidimensional vision data, creating a need for vision sensors that can efficiently extract features from substantial multidimensional vision data. Vision sensors are able to transform the unstructured visual scene into featured information without relying on sophisticated algorithms and complex hardware. The response characteristics of sensors can be abstracted into operators with specific functionalities, allowing for the efficient processing of perceptual information. In this Review, we delve into the hardware implementation of multidimensional vision sensors, exploring their working mechanisms and design principles. We exemplify multidimensional vision sensors built on emerging devices and silicon-based system integration. We further provide benchmarking metrics for multidimensional vision sensors and conclude with the principle of device-system co-design and co-optimization.

Attojoule Hexagonal Boron Nitride-Based Memristor for High-Performance Neuromorphic Computing

In next-generation neuromorphic computing applications, the primary challenge lies in achieving energy-efficient and reliable memristors while minimizing their energy consumption to a level comparable to that of biological synapses. In this work, hexagonal boron nitride (h-BN)-based metal-insulator-semiconductor (MIS) memristors operating is presented at the attojoule-level tailored for high-performance artificial neural networks. The memristors benefit from a wafer-scale uniform h-BN resistive switching medium grown directly on a highly doped Si wafer using metal-organic chemical vapor deposition (MOCVD), resulting in outstanding reliability and low variability. Notably, the h-BN-based memristors exhibit exceptionally low energy consumption of attojoule levels, coupled with fast switching speed. The switching mechanisms are systematically substantiated by electrical and nano-structural analysis, confirming that the h-BN layer facilitates the resistive switching with extremely low high resistance states (HRS) and the native SiOx on Si contributes to suppressing excessive current, enabling attojoule-level energy consumption. Furthermore, the formation of atomic-scale conductive filaments leads to remarkably fast response times within the nanosecond range, and allows for the attainment of multi-resistance states, making these memristors well-suited for next-generation neuromorphic applications. The h-BN-based MIS memristors hold the potential to revolutionize energy consumption limitations in neuromorphic devices, bridging the gap between artificial and biological synapses.

Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth

Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.

Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics

Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.

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.

Reliability effects of lateral filament confinement by nano-scaling the oxide in memristive devices

Write-variability and resistance instability are major reliability concerns impeding implementation of oxide-based memristive devices in neuromorphic systems. The root cause of the reliability issues is the stochastic nature of conductive filament formation and dissolution, whose impact is particularly critical in the high resistive state (HRS). Optimizing the filament stability requires mitigating diffusive processes within the oxide, but these are unaffected by conventional electrode scaling. Here we propose a device design that laterally confines the switching oxide volume and thus the filament to 10 nm, which yields reliability improvements in our measurements and simulations. We demonstrate a 50% decrease in HRS write-variability for an oxide nano-fin device in our full factorial analysis of modulated current-voltage sweeps. Furthermore, we use ionic noise measurements to quantify the HRS filament stability against diffusive processes. The laterally confined filaments exhibit a change in the signal-to-noise ratio distribution with a shift to higher values. Our complementing kinetic Monte Carlo simulation of oxygen vacancy (re-)distribution for confined filaments shows improved noise behavior and elucidates the underlying physical mechanisms. While lateral oxide volume scaling down to filament sizes is challenging, our efforts motivate further examination and awareness of filament confinement effects in regards to reliability.

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

Versatile Cu2ZnSnS4-based synaptic memristor for multi-field-regulated neuromorphic applications

Integrating both electrical and light-modulated multi-type neuromorphic functions in a single synaptic memristive device holds the most potential for realizing next-generation neuromorphic systems, but is still challenging yet achievable. Herein, a simple bi-terminal optoelectronic synaptic memristor is newly proposed based on kesterite Cu2ZnSnS4, exhibiting stable nonvolatile resistive switching with excellent spatial uniformity and unique optoelectronic synaptic behaviors. The device demonstrates not only low switching voltage (-0.39 ± 0.08 V), concentrated Set/Reset voltage distribution (<0.08/0.15 V), and long retention time (>104 s) but also continuously modulable conductance by both electric (different width/interval/amplitude) and light (470-808 nm with different intensity) stimulus. These advantages make the device good electrically and optically simulated synaptic functions, including excitatory and inhibitory, paired-pulsed facilitation, short-/long-term plasticity, spike-timing-dependent plasticity, and "memory-forgetting" behavior. Significantly, decimal arithmetic calculation (addition, subtraction, and commutative law) is realized based on the linear conductance regulation, and high precision pattern recognition (>88%) is well achieved with an artificial neural network constructed by 5 × 5 × 4 memristor array. Predictably, such kesterite-based optoelectronic memristors can greatly open the possibility of realizing multi-functional neuromorphic systems.

A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.

Multiphase Reset Induced Reliable Dual-Mode Resistance Switching of the Ta/HfO2/RuO2 Memristor

Higher functionality should be achieved within the device-level switching characteristics to secure the operational possibility of mixed-signal data processing within a memristive crossbar array. This work investigated electroforming-free Ta/HfO2/RuO2 resistive switching devices for digital- and analog-type applications through various structural and electrical analyses. The multiphase reset behavior, induced by the conducting filament modulation and oxygen vacancy generation (annihilation) in the HfO2 layer by interacting with the Ta (RuO2) electrode, was utilized for the switching mode change. Therefore, a single device can manifest stable binary switching between low and high resistance states for the digital mode and the precise 8-bit conductance modulation (256 resistance values) via an optimized pulse application for the analog mode. An in-depth analysis of the operation in different modes and comparing memristors with different electrode structures validate the proposed mechanism. The Ta/HfO2/RuO2 resistive switching device is feasible for a mixed-signal processable memristive array.

A Fully-Integrated Memristor Chip for Edge Learning

The fully-integrated memristor chip for edge learning provides a solid foundation for neural network computation. The fully-integrated memristor chip enables efficient object recognition in noisy backgrounds while minimizing energy consumption. The computing-in-memory chip represents an innovative and interdisciplinary technology that extends beyond multiple research domains.

Memristor-Based Neuromorphic Chips

In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.

Molecular Electronics: From Nanostructure Assembly to Device Integration

Integrated electronics and optoelectronics based on organic semiconductors have attracted considerable interest in displays, photovoltaics, and biosensing owing to their designable electronic properties, solution processability, and flexibility. Miniaturization and integration of devices are growing trends in molecular electronics and optoelectronics for practical applications, which requires large-scale and versatile assembly strategies for patterning organic micro/nano-structures with simultaneously long-range order, pure orientation, and high resolution. Although various integration methods have been developed in past decades, molecular electronics still needs a versatile platform to avoid defects and disorders due to weak intermolecular interactions in organic materials. In this perspective, a roadmap of organic integration technologies in recent three decades is provided to review the history of molecular electronics. First, we highlight the importance of long-range-ordered molecular packing for achieving exotic electronic and photophysical properties. Second, we classify the strategies for large-scale integration of molecular electronics through the control of nucleation and crystallographic orientation, and evaluate them based on factors of resolution, crystallinity, orientation, scalability, and versatility. Third, we discuss the multifunctional devices and integrated circuits based on organic field-effect transistors (OFETs) and photodetectors. Finally, we explore future research directions and outlines the need for further development of molecular electronics, including assembly of doped organic semiconductors and heterostructures, biological interfaces in molecular electronics and integrated organic logics based on complementary FETs.

Local prediction-learning in high-dimensional spaces enables neural networks to plan

Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning.

Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating

Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.

Programming memristor arrays with arbitrarily high precision for analog computing

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.

Stripe noise removal in conductive atomic force microscopy

Conductive atomic force microscopy (c-AFM) can provide simultaneous maps of the topography and electrical current flow through materials with high spatial resolution and it is playing an increasingly important role in the characterization of novel materials that are being investigated for novel memory devices. However, noise in the form of stripe features often appear in c-AFM images, challenging the quantitative analysis of conduction or topographical information. To remove stripe noise without losing interesting information, as many as sixteen destriping methods are investigated in this paper, including three additional models that we propose based on the stripes characteristics, and thirteen state-of-the-art destriping methods. We have also designed a gradient stripe noise model and obtained a ground truth dataset consisting of 800 images, generated by rotating and cropping a clean image, and created a noisy image dataset by adding random intensities of simulated noise to the ground truth dataset. In addition to comparing the results of the stripe noise removal visually, we performed a quantitative image quality comparison using simulated datasets and 100 images with very different strengths of simulated noise. All results show that the Low-Rank Recovery method has the best performance and robustness for removing gradient stripe noise without losing useful information. Furthermore, a detailed performance comparison of Polynomial fitting and Low-Rank Recovery at different levels of real noise is presented.

Hydrogen-triggered metal filament rupture in Cu-based resistance switches

Cation-based resistance switches have been considered as promising candidates for memory cells and other novel devices. So far, the most accepted switching processes of such devices are based on the formation/rupture of metallic filaments between two electrodes. Although many recent studies have identified the existence of H2O (and resulting -OH groups) in such devices, their effects on the switching process are still unclear. In the present work, by taking Cu/Ta2O5/Pt device as an example, we have theoretically revealed that H ions may dissociate from -OH groups and accumulate onto the Cu filament in amorphous Ta2O5. After that, the adsorbed H ions will induce a series of changes, such as the elongation of the adjacent Cu-Cu bonds, the weakening of the Cu-Cu bonds, the increase of charge on Cu cations, and the enhancement of diffusivities of Cu cations, all of which eventually lead to the rupture of the Cu filament. Interestingly, our proposed 'H-triggered metal filament rupture' model is similar to the widely studied 'hydrogen embrittlement phenomenon'. The crucial point of this model is the high catalytic activity of Cu towards the splitting of -OH group. Consequently, it is expected that this model could be applicable to other Cu-cation based resistance switches.

Memristor-based storage system with convolutional autoencoder-based image compression network

The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network to boost the energy efficiency and speed of the image compression/retrieval and improve the storage density. We adopt the 4-bit memristor arrays to experimentally demonstrate the functions of the system. We propose a step-by-step quantization aware training scheme and an equivalent transformation for transpose convolution to improve the system performance. The system exhibits a high (>33 dB) peak signal-to-noise ratio in the compression and decompression of the ImageNet and Kodak24 datasets. Benchmark comparison results show that the 4-bit memristor-based storage system could reduce the latency and energy consumption by over 20×/5.6× and 180×/91×, respectively, compared with the server-grade central processing unit-based/the graphics processing unit-based processing system, and improve the storage density by more than 3 times.

Proposition of Adaptive Read Bias: A Solution to Overcome Power and Scaling Limitations in Ferroelectric-Based Neuromorphic System

Hardware neuromorphic systems are crucial for the energy-efficient processing of massive amounts of data. Among various candidates, hafnium oxide ferroelectric tunnel junctions (FTJs) are highly promising for artificial synaptic devices. However, FTJs exhibit non-ideal characteristics that introduce variations in synaptic weights, presenting a considerable challenge in achieving high-performance neuromorphic systems. The primary objective of this study is to analyze the origin and impact of these variations in neuromorphic systems. The analysis reveals that the major bottleneck in achieving a high-performance neuromorphic system is the dynamic variation, primarily caused by the intrinsic 1/f noise of the device. As the device area is reduced and the read bias (VRead ) is lowered, the intrinsic noise of the FTJs increases, presenting an inherent limitation for implementing area- and power-efficient neuromorphic systems. To overcome this limitation, an adaptive read-biasing (ARB) scheme is proposed that applies a different VRead to each layer of the neuromorphic system. By exploiting the different noise sensitivities of each layer, the ARB method demonstrates significant power savings of 61.3% and a scaling effect of 91.9% compared with conventional biasing methods. These findings contribute significantly to the development of more accurate, efficient, and scalable neuromorphic systems.

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co-Fe-B)x(LiNbO3)100-x nanocomposite memristors

MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of a neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software realization demonstrates high classification accuracy, although the number of trainable weights is relatively low. One more promising way of improving the NCS performance, especially in terms of power consumption, is its hardware realization using memristors. Therefore, in this work, we proposed an NCS with an adapted MLP-Mixer architecture and memristive weights. For this purpose, we used a passive crossbar array of (Co-Fe-B)x(LiNbO3)100-x memristors. Firstly, we studied the characteristics of such memristors, including their minimal resistive switching time, which was extrapolated to be in the picosecond range. Secondly, we created a fully hardware NCS with memristive weights that are capable of classification of simple 4-bit vectors. The system was shown to be robust to noise introduction in the input patterns. Finally, we used experimental memristive characteristics to simulate an adapted MLP-Mixer architecture that demonstrated a classification accuracy of (94.7 ± 0.3)% on the Modified National Institute of Standards and Technology (MNIST) dataset. The obtained results are the first steps toward the realization of memristive NCS with a promising MLP-Mixer architecture.

Self-Rectifying Memristors for Three-Dimensional In-Memory Computing

Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102  Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.

Engineering Atomic-Scale Patterning and Resistive Switching in 2D Crystals and Application in Image Processing

The ultrathin thickness of 2D layered materials affords the control of their properties through defects, surface modification, and electrostatic fields more efficiently compared with bulk architecture. In particular, patterning design, such as moiré superlattice patterns and spatially periodic dielectric structures, are demonstrated to possess the ability to precisely control the local atomic and electronic environment at large scale, thus providing extra degrees of freedom to realize tailored material properties and device functionality. Here, the scalable atomic-scale patterning in superionic cuprous telluride by using the bonding difference at nonequivalent copper sites is reported. Moreover, benefitting from the natural coupling of ordered and disordered sublattices, controllable piezoelectricity-like multilevel switching and bipolar switching with the designed crystal structure and electrical contact is realized, and their application in image enhancement is demonstrated. This work extends the known classes of patternable crystals and atomic switching devices, and ushers in a frontier for image processing with memristors.

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.

Bi2O2Se-Based Bimode Noise Generator for the Application of Generative Adversarial Networks

In the emerging technology, the generative aversive networks (GANs), randomness, and unpredictability of inputting noises are the keys to the uniqueness, diversity, robustness, and security of the generated images. Compared with deterministic software-based noise generation, hardware-based noise generation introduces physical entropy sources, such as electronic and photonic noises, to add unpredictability. In this study, bimode Bi2O2Se-based noise generators have been demonstrated for the application of GANs. Harnessing its ultrahigh carrier mobility, excellent air stability, marvelous optoelectronic performance, as well as the unique surface resistive switching effect and defect locations in the energy diagram, Bi2O2Se provides a good material platform to easily integrate with multiple device architectures for generating noises in different physical sources. The noise of the black current mode in a photodetector architecture and the random telegraph noise in a memristor mode were measured, characterized, compared, and analyzed. A method of Markov chain equipped with K-means clustering was carried out to calculate the discrete noise states and the transition probability matrix between them. To evaluate the generated properties of the GANs based on the hardware noise source, the inception score and Fréchet inception distance were evaluated.

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era

Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.

Controllability of the Conductive Filament in Porous SiOx Memristors by Humidity-Mediated Silver Ion Migration

Oxide-based memristors composed of Ag/porous SiOx/Si stacks are fabricated using different etching time durations between 0 and 90 s, and the memristive properties are analyzed in the relative humidity (RH) range of 30-60%. The combination of humidity and porous structure provides binding sites to control silver filament formation with a confined nanoscale channel. The memristive properties of devices show high on/off ratios up to 108 and a dispersion coefficient of 0.1% of the high resistance state (CHRS) when the RH increases to 60%. Humidity-mediated silver ion migration in the porous SiOx memristors is investigated, and the mechanism leading to the synergistic effects between the porous structure and environmental humidity is elucidated. The artificial neural network constructed theoretically shows that the recognition rate increases from 60.9 to 85.29% in the RH range of 30-60%. The results and theoretical understanding provide insights into the design and optimization of oxide-based memristors in neuromorphic computing applications.

Open-loop analog programmable electrochemical memory array

Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.

Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar

Combinatorial optimization problems are prevalent in various fields, but obtaining exact solutions remains challenging due to the combinatorial explosion with increasing problem size. Special-purpose hardware such as Ising machines, particularly memristor-based analog Ising machines, have emerged as promising solutions. However, existing simulate-annealing-based implementations have not fully exploited the inherent parallelism and analog storage/processing features of memristor crossbar arrays. This work proposes a quantum-inspired parallel annealing method that enables full parallelism and improves solution quality, resulting in significant speed and energy improvement when implemented in analog memristor crossbars. We experimentally solved tasks, including unweighted and weighted Max-Cut and traveling salesman problem, using our integrated memristor chip. The quantum-inspired parallel annealing method implemented in memristor-based hardware has demonstrated significant improvements in time- and energy-efficiency compared to previously reported simulated annealing and Ising machine implemented on other technologies. This is because our approach effectively exploits the natural parallelism, analog conductance states, and all-to-all connection provided by memristor technology, promising its potential for solving complex optimization problems with greater efficiency.

Light-enhanced molecular polarity enabling multispectral color-cognitive memristor for neuromorphic visual system

An optoelectronic synapse having a multispectral color-discriminating ability is an essential prerequisite to emulate the human retina for realizing a neuromorphic visual system. Several studies based on the three-terminal transistor architecture have shown its feasibility; however, its implementation with a two-terminal memristor architecture, advantageous to achieving high integration density as a simple crossbar array for an ultra-high-resolution vision chip, remains a challenge. Furthermore, regardless of the architecture, it requires specific material combinations to exhibit the photo-synaptic functionalities, and thus its integration into various systems is limited. Here, we suggest an approach that can universally introduce a color-discriminating synaptic functionality into a two-terminal memristor irrespective of the kinds of switching medium. This is possible by simply introducing the molecular interlayer with long-lasting photo-enhanced dipoles that can adjust the resistance of the memristor at the light-irradiation. We also propose the molecular design principle that can afford this feature. The optoelectronic synapse array having a color-discriminating functionality is confirmed to improve the inference accuracy of the convolutional neural network for the colorful image recognition tasks through a visual pre-processing. Additionally, the wavelength-dependent optoelectronic synapse can also be leveraged in the design of a light-programmable reservoir computing system.

Multi-level resistive switching in hafnium-oxide-based devices for neuromorphic computing

In the growing area of neuromorphic and in-memory computing, there are multiple reviews available. Most of them cover a broad range of topics, which naturally comes at the cost of details in specific areas. Here, we address the specific area of multi-level resistive switching in hafnium-oxide-based devices for neuromorphic applications and summarize the progress of the most recent years. While the general approach of resistive switching based on hafnium oxide thin films has been very busy over the last decade or so, the development of hafnium oxide with a continuous range of programmable states per device is still at a very early stage and demonstrations are mostly at the level of individual devices with limited data provided. On the other hand, it is positive that there are a few demonstrations of full network implementations. We summarize the general status of the field, point out open questions, and provide recommendations for future work.

Atypical Sliding and Moiré Ferroelectricity in Pure Multilayer Graphene

Most nonferroelectric two-dimensional materials can be endowed with so-called sliding ferroelectricity via nonequivalent homobilayer stacking, which is not applicable to monoelement systems like pure graphene bilayer with inversion symmetry at any sliding vector. Herein, we show first-principles evidence that multilayer graphene with N>3 can all be ferroelectric, where the polarizations of polar states stem from the symmetry breaking in stacking configurations of across layer instead of adjacent layer, which are electrically switchable via interlayer sliding. The nonpolar states can also be electrically driven to polar states via sliding, and more diverse states with distinct polarizations will emerge in more layers. In contrast to the ferroelectric moiré domains with opposite polarization directions in twisted bilayers reported previously, the moiré pattern in some multilayer graphene systems (e.g., twisted monolayer-trilayer graphene) possess nonzero net polarizations with domains of the same direction separated by nonpolar regions, which can be electrically reversed upon interlayer sliding. The distinct moiré bands of two polar states should facilitate electrical detection of such sliding moiré ferroelectricity during switching.