High-Performance Memristors Based on Ultrathin 2D Copper Chalcogenides

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

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

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.

Two-Dimensional Semiconductors and Transistors for Future Integrated Circuits

Silicon transistors are approaching their physical limit, calling for the emergence of a technological revolution. As the acknowledged ultimate version of transistor channels, 2D semiconductors are of interest for the development of post-Moore electronics due to their useful properties and all-in-one potentials. Here, the promise and current status of 2D semiconductors and transistors are reviewed, from materials and devices to integrated applications. First, we outline the evolution and challenges of silicon-based integrated circuits, followed by a detailed discussion on the properties and preparation strategies of 2D semiconductors and van der Waals heterostructures. Subsequently, the significant progress of 2D transistors, including device optimization, large-scale integration, and unconventional devices, are presented. We also examine 2D semiconductors for advanced heterogeneous and multifunctional integration beyond CMOS. Finally, the key technical challenges and potential strategies for 2D transistors and integrated circuits are also discussed. We envision that the field of 2D semiconductors and transistors could yield substantial progress in the upcoming years and hope this review will trigger the interest of scientists planning their next experiment.

Fabrication of high quality lead-free double perovskite Cs2AgBiBr6thin film and its application in memristor with ultralow operation voltage

Recently, the lead-free double perovskite Cs2AgBiBr6has been considered as a promising candidate for next-generation nonvolatile memory and artificial synapse devices due to its high stability and low toxicity compared to its lead-based counterparts. In this work, we developed a simple and effective method to produce high-quality lead-free double perovskite Cs2AgBiBr6thin films without pinholes and particles by applying a low-pressure assisted method under ambient condition with a relative humidity (RH) of about 45%. The formation of pinholes and Ag precipitation in the perovskite Cs2AgBiBr6 films is effectively suppressed by the proper ratio of N,N-dimenthylformamide (DMF) mixed in dimethyl sulfoxide (DMSO) solvents. Furthermore, the grain size of the Cs2AgBiBr6films can be significantly increased by increasing the post-annealing temperature. Finally, a sandwiched structure memristor with an ITO/Cs2AgBiBr6/Ta configuration was successfully demonstrated, featuring ultralow operation voltage (VSet∼ 57 ± 23 mV,VReset∼ -692 ± 68 mV) and satisfactory memory window (the ratio ofRHRS/RLRS∼ 10 times), which makes it suitable for low-power consumption information storage devices.

Asymmetric Monolayer Mesoporous Nanosheets of Regularly Arranged Semi-Opened Pores via a Dual-Emulsion-Directed Micelle Assembly

Constructing asymmetric two-dimensional (2D) mesoporous nanomaterials with new pore structure, tunable monolayer architectures, and especially anisotropic surfaces remains a great challenge in materials science. Here, we report a dual-emulsion directed micelle assembly approach to fabricate a novel type of asymmetric monolayer mesoporous organosilica nanosheet for the first time. In this asymmetric 2D structure, numerous quasi-spherical semiopened mesopores (∼20 nm in diameter, 24 nm in opening size) were regularly arranged on a plane, endowing the porous nanosheets (several micrometers in size) with a typical surface anisotropy on two sides. Meanwhile, lots of triangular intervoids (4.0-5.0 nm in size) can also be found among each three semiopened mesopores, enabling the nanosheet to be interconnected. Vitally, such interconnected, anisotropic porous nanosheets exhibit ultrahigh accessible surface area (∼714 m2 g-1) and good lipophilicity properties owing to the abundant semiopened mesopores. Additionally, besides the nanosheet, the configuration of the asymmetric porous structure can also be transformed into a microcapsule when controlling the emulsification size via a facile ultrasonic treatment. As a demonstration, we show that the asymmetric microcapsule shows a high demulsification efficiency (>98%) and cyclic stability (>6 recycle times). Our protocol opens up a new avenue for developing next-generation asymmetric mesoporous materials for various applications.

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.

Substrate Engineering for Chemical Vapor Deposition Growth of Large-Scale 2D Transition Metal Dichalcogenides

The large-scale production of 2D transition metal dichalcogenides (TMDs) is essential to realize their industrial applications. Chemical vapor deposition (CVD) has been considered as a promising method for the controlled growth of high-quality and large-scale 2D TMDs. During a CVD process, the substrate plays a crucial role in anchoring the source materials, promoting the nucleation and stimulating the epitaxial growth. It thus significantly affects the thickness, microstructure, and crystal quality of the products, which are particularly important for obtaining 2D TMDs with expected morphology and size. Here, an insightful review is provided by focusing on the recent development associated with the substrate engineering strategies for CVD preparation of large-scale 2D TMDs. First, the interaction between 2D TMDs and substrates, a key factor for the growth of high-quality materials, is systematically discussed by combining the latest theoretical calculations. Based on this, the effect of various substrate engineering approaches on the growth of large-area 2D TMDs is summarized in detail. Finally, the opportunities and challenges of substrate engineering for the future development of 2D TMDs are discussed. This review might provide deep insight into the controllable growth of high-quality 2D TMDs toward their industrial-scale practical applications.

Phase Engineering of Nonstoichiometric Cu2-xSe as Anode for Aqueous Zn-Ion Batteries

Aqueous zinc-ion batteries (AZIBs) are receiving widespread attention due to their abundant resources, low material cost, and high safety. However, the susceptibility of Zn metal anodes to corrosion and hydrogen evolution limits their further practical applications. Replacing Zn metal with intercalation-type anode material and constructing rocking-chair-type batteries could be an effective way to significantly prolong the cycle life of AZIBs. Herein, we present copper selenide with different crystal phase structures through a facile redox reaction as an anode for AZIBs. By comparing and analyzing different copper selenide phases, it is found that the cubic Cu2-xSe shows superior structural stability and highly reversible Zn2+ storage. Theoretical calculation results further demonstrate that the cubic Cu2-xSe possesses an increased electrical conductivity, higher Zn2+ adsorption energy, and reduced diffusion barrier, thereby promoting the storage reversibility and (de)intercalation kinetics of the Zn2+ ion. Thus, the Cu2-xSe anode delivers a long-term service life of over 15 000 cycles and impressive cumulative capacity. Furthermore, the full-cells assembled with the MnO2/CNT cathode operate stably for over 1500 cycles at 6 mA cm-2 at a negative/positive (N/P) capacity ratio of ∼1.53. This work provides a more ideal Zn-metal-free anode, which helps to push the practical applications of AZIBs.

Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio >104

Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation <0.08/0.02 V), high On/Off ratio (>104), and long retention time (>104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470-655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

Multiple-State Nonvolatile Memory Based on Ultrathin Indium Oxide Film via Liquid Metal Printing

In this work, the ultrathin two-dimensional (2D) indium oxide (InO_x) with a large area of more than 100 μm² and a high degree of uniformity was automatically peeled off from indium by the liquid-metal printing technique. Raman and optical measurements revealed that 2D-InO_x has a polycrystalline cubic structure. By altering the printing temperature which affects the crystallinity of 2D-InO_x, the mechanism of the existence and disappearance of memristive characteristics was established. The tunable characteristics of the 2D-InO_x memristor with reproducible one-order switching was manifest from the electrical measurements. Further adjustable multistate characteristics of the 2D-InO_x memristor and its resistance switching mechanism were evaluated. A detailed examination of the memristive process demonstrated the Ca²⁺ mimic dynamic in 2D-InO_x memristors as well as the fundamental principles underlying biological and artificial synapses. These surveys allow us to comprehend a 2D-InO_x memristor using the liquid-metal printing technique and could be applied to future neuromorphic applications and in the field of revolutionary 2D material exploration.

Van der Waals Epitaxy Growth of 2D Single-Element Room-Temperature Ferromagnet

2D single-element materials, which are pure and intrinsically homogeneous on the nanometer scale, can cut the time-consuming material-optimization process and circumvent the impure phase, bringing about opportunities to explore new physics and applications. Herein, for the first time, the synthesis of ultrathin cobalt single-crystalline nanosheets with a sub-millimeter scale via van der Waals epitaxy is demonstrated. The thickness can be as low as ≈6 nm. Theoretical calculations reveal their intrinsic ferromagnetic nature and epitaxial mechanism: that is, the synergistic effect between van der Waals interactions and surface energy minimization dominates the growth process. Cobalt nanosheets exhibit ultrahigh blocking temperatures above 710 K and in-plane magnetic anisotropy. Electrical transport measurements further reveal that cobalt nanosheets have significant magnetoresistance (MR) effect, and can realize a unique coexistence of positive MR and negative MR under different magnetic field configurations, which can be attributed to the competition and cooperation effect among ferromagnetic interaction, orbital scattering, and electronic correlation. These results provide a valuable case for synthesizing 2D elementary metal crystals with pure phase and room-temperature ferromagnetism and pave the way for investigating new physics and related applications in spintronics.

Scalable Edge-Oriented Metallic Two-Dimensional Layered Cu2Te Arrays for Electrocatalytic CO2 Methanation

Copper-based nanomaterials are compelling for high-efficient, low-cost electrocatalytic CO₂ reduction reaction (CO₂RR) due to their exotic electronic and structural properties. However, controllable preparation of copper-based two-dimensional (2D) materials with abundant catalytically active sites, that guarantee high CO₂RR performance, remains challenging, especially on a large scale. Here, an in situ vertical growth of scalable metallic 2D Cu₂Te nanosheet arrays on commercial copper foils is demonstrated for efficient CO₂-to-CH₄ electrocatalysis. The edge-oriented growth of Cu₂Te nanosheets with tunable sizes and thicknesses is facilely attained by a two-step process of chemical etching and chemical vapor deposition. These active sites abounding on highly exposed edges of Cu₂Te nanosheets greatly promote the electroreduction of CO₂ into CH₄ at a potential as low as -0.4 V (versus the reversible hydrogen electrode), while suppressing hydrogen evolution reaction. When a flow cell is employed to accelerate the mass transfer, the faradaic efficiency reaches ∼63% at an applied current density of 300 mA cm^-2. These findings will provide great possibilities for developing scalable, energy-efficient Cu-based CO₂RR electrocatalysts.

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

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

Layer-Structured Anisotropic Metal Chalcogenides: Recent Advances in Synthesis, Modulation, and Applications

The unique electronic and catalytic properties emerging from low symmetry anisotropic (1D and 2D) metal chalcogenides (MCs) have generated tremendous interest for use in next generation electronics, optoelectronics, electrochemical energy storage devices, and chemical sensing devices. Despite many proof-of-concept demonstrations so far, the full potential of anisotropic chalcogenides has yet to be investigated. This article provides a comprehensive overview of the recent progress made in the synthesis, mechanistic understanding, property modulation strategies, and applications of the anisotropic chalcogenides. It begins with an introduction to the basic crystal structures, and then the unique physical and chemical properties of 1D and 2D MCs. Controlled synthetic routes for anisotropic MC crystals are summarized with example advances in the solution-phase synthesis, vapor-phase synthesis, and exfoliation. Several important approaches to modulate dimensions, phases, compositions, defects, and heterostructures of anisotropic MCs are discussed. Recent significant advances in applications are highlighted for electronics, optoelectronic devices, catalysts, batteries, supercapacitors, sensing platforms, and thermoelectric devices. The article ends with prospects for future opportunities and challenges to be addressed in the academic research and practical engineering of anisotropic MCs.

Low-Power Memristor Based on Two-Dimensional Materials

The memristor is an excellent candidate for nonvolatile memory and neuromorphic computing. Recently, two-dimensional (2D) materials have been developed for use in memristors with high-performance resistive switching characteristics, such as high on/off ratios, low SET/RESET voltages, good retention and endurance, fast switching speed, and low power and energy consumption. Low-power memristors are highly desired for recent fast-speed and energy-efficient artificial neuromorphic networks. This Perspective focuses on the recent progress of low-power memristors based on 2D materials, providing a condensed overview of relevant developments in memristive performance, physical mechanism, material modification, and device assembly as well as potential applications. The detailed research status of memristors has been reviewed based on different 2D materials from insulating hexagonal boron nitride, semiconducting transition metal dichalcogenides, to some newly developed 2D materials. Furthermore, a brief summary introducing the perspectives and challenges is included, with the aim of providing an insightful guide for this research field.

Reliable Memristor Based on Ultrathin Native Silicon Oxide

Memristors based on two-dimensional (2D) materials can exhibit great scalability and ultralow power consumption, yet the structural and thickness inhomogeneity of ultrathin electrolytes lowers the production yield and reliability of devices. Here, we report that the self-limiting amorphous SiO_x (∼2.7 nm) provides a perfect atomically thin electrolyte with high uniformity, featuring a record high production yield. With the guidance of physical modeling, we reveal that the atomic thickness of SiO_x enables anomalous resistive switching with a transition to an analog quasi-reset mode, where the filament stability can be further enhanced using Ag-Au nanocomposite electrodes. Such a picojoule memristor shows record low switching variabilities (C2C and D2D variation down to 1.1 and 2.6%, respectively), good retention at a few microsiemens, and high conductance-updating linearity, constituting key metrics for analog neural networks. In addition, the stable high-resistance state is found to be an excellent source for true random numbers of Gaussian distribution. This work opens up opportunities in mass production of Si-compatible memristors for ultradense neuromorphic and security hardware.