Memristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal‐ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low‐power consumption requirements. Therefore, it is very necessary to develop new materials to realize memristor devices that are different from the mechanisms of oxygen vacancy or metal‐ion conductive filaments to realize low‐power operation. Herein, high‐performance and low‐power consumption memristors based on 2D WS2 with 2H phase are demonstrated, which show fast ON (OFF) switching times of 13 ns (14 ns), low program current of 1 µA in the ON state, and SET (RESET) energy reaching the level of femtojoules. Moreover, the memristor can mimic basic biological synaptic functions. Importantly, it is proposed that the generation of sulfur and tungsten vacancies and electron hopping between vacancies are dominantly responsible for the resistance switching performance. Density functional theory calculations show that the defect states formed by sulfur and tungsten vacancies are at deep levels, which prevent charge leakage and facilitate the realization of low‐power consumption for neuromorphic computing application.

Resistive switching (RS) is a promising emerging storage technology that has received much attention due to its many advantages, such as economy, fast operating speed, long retention, high density, and low energy consumption. [1] RS effects are widely applied in the fields of nonvolatile RS random access memory (RRAM), artificial neural computing, and reconfigurable logic operations and so on. [2] RRAM memories based on electrochemical metallization (ECM) and valance change mechanism (VCM) are commonly used for the memristor application. [3] Compared with conventional computing based on the von Neumann architecture, memristor (i.e., RS device) computing is proving to be superior for brain-inspired computing, such as image processing and speech recognition, [2] where the diffusion of the metal ions such as Ag + , Cu + , or oxygen vacancies are used to mimic the diffusion of Ca + in the neural cell. [4] However, the switching voltages in the memristor devices (MDs) showWith the advent of the era of big data, resistive random access memory (RRAM) has become one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of information. However, the switching voltage of the RRAM MDs shows a very broad distribution due to the random formation of the conductive filaments. Here, selfassembled lead sulfide (PbS) quantum dots (QDs) are used to improve the uniformity of switching parameters of RRAM, which is very simple comparing with other methods. The resistive switching (RS) properties of the MD with the self-assembled PbS QDs exhibit better performance than those of MDs with pure-Ga 2 O 3 and randomly distributed PbS QDs, such as a reduced threshold voltage, uniformly distributed SET and RESET voltages, robust retention, fast response time, and low power consumption. This enhanced performance may be attributed to the ordered arrangement of the PbS QDs in the self-assembled PbS QDs which can efficiently guide the growth direction for the conducting filaments. Moreover, biosynaptic functions and plasticity, are implemented successfully in the MD with the self-assembled PbS QDs. This work offers a new method of improving memristor performance, which can significantly expand existing applications and facilitate the development of artificial neural systems. Data Storage

The development of the information age has made resistive random access memory (RRAM) a critical nanoscale memristor device (MD). However, due to the randomness of the area formed by the conductive filaments (CFs), the RRAM MD still suffers from a problem of insufficient reliability. In this study, the memristor of Ag/ ZrO 2 /WS 2 /Pt structure is proposed for the first time, and a layer of two-dimensional (2D) WS 2 nanosheets was inserted into the MD to form 2D material and oxide double-layer MD (2DOMD) to improve the reliability of single-layer devices. The results indicate that the electrochemical metallization memory cell exhibits a highly stable memristive switching and concentrated ON-and OFF-state voltage distribution, high speed (∼10 ns), and robust endurance (>10 9 cycles). This result is superior to MDs with a single-layer ZrO 2 or WS 2 film because two layers have different ion transport rates, thereby limiting the rupture/rejuvenation of CFs to the bilayer interface region, which can greatly reduce the randomness of CFs in MDs. Moreover, we used the handwritten recognition dataset (i.e., the Modified National Institute of Standards and Technology (MNIST) database) for neuromorphic simulations. Furthermore, biosynaptic functions and plasticity, including spike-timing-dependent plasticity and paired-pulse facilitation, have been successfully achieved. By incorporating 2D materials and oxides into a doublelayer MD, the practical application of RRAM MD can be significantly enhanced to facilitate the development of artificial synapses for brain-enhanced computing systems in the future.

Two‐dimensional (2D) materials have attracted extensive research interest in academia due to their excellent electrochemical properties and broad application prospects. Among them, 2D transition metal carbides (Ti3C2Tx) show semiconductor characteristics and are studied widely. However, there are few academic reports on the use of 2D MXene materials as memristors. In this work, reported is a memristor based on MXene Ti3C2Tx flakes. After electroforming, Al/Ti3C2Tx/Pt devices exhibit repeatable resistive switching (RS) behavior. More interestingly, the resistance of this device can be continuously modulated under the pulse sequence with 10 ns pulse width, and the pulse width of 10 ns is much lower than that in other reported work. Moreover, on the nanosecond scale, the transition from short‐term plasticity to long‐term plasticity is achieved. These two properties indicate that this device is favorable for ultrafast biological synapse applications and high‐efficiency training of neural networks. Through the exploration of the microstructure, Ti vacancies and partial oxidation are proposed as the origins of the physical mechanism of RS behavior. This work reveals that 2D MXene Ti3C2Tx flakes have excellent potential for use in memristor devices, which may open the door for more functions and applications.

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.

Utilizing the instability of the edge atoms of graphene defects, carbon conductive filaments were formed under the regulation of the electric field and the synaptic function was achieved.

A model based on carbon conductive filaments (CFs) for a memristor based on carbon quantum dots (QDs) is proposed for the first time.

An electrochemical metallization memristor based on Zr0.5Hf0.5O2 film and an active Cu electrode with quantum conductance and neuromorphic behavior has been reported in this work.