Search

While transition‐metal thiophosphate (MPX3) materials have been a subject of extensive research in recent years, experimental studies on MPX3‐based memristors are still in their early stages, with device performance being less than ideal. Here, the successful fabrication of high‐yield, high‐performance, and uniform memristors are demonstrated to possess desired characteristics for neuromorphic computing using a single‐crystalline few‐layered manganese phosphorus trisulfide (MnPS3) as a resistive switching medium. The Ti/MnPS3/Au memristor exhibits small switching voltage (<1 V), long memory retention (104 s), fast switching speed (≈20 ns), high On/Off ratio (nearly two orders of magnitude), and simultaneously achieves emulation of synaptic weight plasticity. The microscopic investigation of the structural and chemical characteristics of the few‐layer MnPS3 reveals the presence of structural defects and residual Ti throughout the stacked layer following the application of voltage, which contributes to the uniformity of switching with a low set voltage. With highly linear and symmetric analog weight updates coupled with the capability of accurate decimal arithmetic operations, a high accuracy of 95.15% in supervised learning using the MNIST handwritten recognition dataset is achieved in the artificial neural network. Furthermore, convolutional image processing can be implemented using hardware multiply‐and‐accumulate operation in an experimental memristor crossbar array. [ABSTRACT FROM AUTHOR]

For Schottky barrier‐modulated memristors based on 2D semiconductors, it has, to date, not been possible to achieve control over defect type and concentration as the measured switching characteristics vary considerably even under similar fabrication conditions. In this work, four distinct types of memristors are identified based on the combination of low and high resistance sequences, as well as volatile and nonvolatile characteristics. All these four types of memristors were previously observed experimentally by different research labs. It is found that the specific behavior of each memristor type can be explained by the Schottky barrier height modulation and current rectification arising from the concerted effects of the concentration, charge polarity and mobility of defects. The conditions required to realize the four types of 2D semiconductor‐based memristors are analyzed and design guidelines for fabricating each of these four types of memristors are provided. [ABSTRACT FROM AUTHOR]

Memristors are intensively studied as being regarded as the critical components to realize the in‐memory computing paradigm. A novel electrochemical metallization memristor based on solution‐processed Pt/CuI/Cu structure is proposed and demonstrated in this work, with a high resistance switching ratio of 1.53 × 107. Owing to the efficient drift paths provided by Cu vacancies for Cu cations in CuI, very small operating voltages (Vset = 0.64 V and Vreset = −0.19 V) are characterized, contributing to ultralow standby power consumption of 9 fW and per set transition of 8.73 µW. Using CuI memristor arrays, a set of Boolean logic operations and a half‐adder are implemented. Moreover, by building the model for a 75 × 48 one‐transistor‐one‐memristor array, the feasibility of hardware encryption and decryption for images is verified. All these demonstrate that solution‐processed CuI memristors possess great potential in constructing energy‐efficient logic‐in‐memory computing architectures. [ABSTRACT FROM AUTHOR]

State-of-the-art memristors are mostly formed by vertical metal–insulator–metal (MIM) structure, which rely on the formation of conductive filaments for resistive switching (RS). However, owing to the stochastic formation of filament, the set/reset voltage of vertical MIM memristors is difficult to control, which results in poor temporal and spatial switching uniformity. Here, a two-terminal lateral memristor based on electron-beam-irradiated rhenium disulfide (ReS2) is realized, which unveils a resistive switching mechanism based on Schottky barrier height (SBH) modulation. The devices exhibit a forming-free, stable gradual RS characteristic, and simultaneously achieve a small transition voltage variation during positive and negative sweeps (6.3%/5.3%). The RS is attributed to the motion of sulfur vacancies induced by voltage bias in the device, which modulates the ReS2/metal SBH. The gradual SBH modulation stabilizes the temporal variation in contrast to the abrupt RS in MIM-based memristors. Moreover, the emulation of long-term synaptic plasticity of biological synapses is demonstrated using the device, manifesting its potential as artificial synapse for energy-efficient neuromorphic computing applications. [ABSTRACT FROM AUTHOR]

Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to overcome the intrinsic energy and speed issues of traditional von Neumann based computing architecture. With the ability to perform vector‐matrix multiplications and flexible tunable conductance, the memristor crossbar array (CBA) structure is one of the most promising candidates to realize neural cognitive systems. The boom in the development of memristive synapses and neurons has propelled the developments of artificial neural networks (ANNs) to emulate the highly hierarchically organized network of human brain in the past decade. To achieve this, realizing large scale, high‐density memristive CBAs is a prerequisite to constructing complex ANNs. Herein, the stringent requirements in device performance and array parameters for hardware ANNs are analyzed, and the efforts in addressing the associated challenges are discussed. Recent progress on the experimental demonstration of neuromorphic computing systems (NCSs) is presented. Recommendations for further performance optimization at the device, circuit, and algorithm levels are proposed. This Report serves as a guide for the hardware implementation of NCS based on large‐scale CBAs. [ABSTRACT FROM AUTHOR]

Memristors with biological synaptic behaviors and functions have been intensively studied as an important component for neuromorphic computing system, which hold promise to address the power consumption issue in modern computers based on von Neumann architecture. However, the resistive switching mechanism that relies on the stochastic formation of conductive filaments leads to poor cycle‐to‐cycle (temporal) and cell‐to‐cell (spatial) variations for filamentary memristors. The emergence of memristors based on 2D ferroelectric materials can potentially avoid these issues. Here, a vertical Au/CuInP2S6 (CIPS)/Ti diode is demonstrated using exfoliated ferroelectric CIPS flake. Through ferroelectric switching, the CIPS diode realizes resistive switching with a ratio larger than 6 × 103. The endurance measurement shows a small set and reset voltage variation of 5.3% and 9.1%, respectively. Key synaptic behaviors including spike‐time‐dependent plasticity, paired‐pulse‐facilitation, and paired‐pulse‐depression are successfully mimicked, manifesting the potential application of CIPS diode in a neuromorphic computing system. Moreover, pattern learning and memory behaviors are emulated using a 3 × 3 CIPS crossbar array. [ABSTRACT FROM AUTHOR]

Photonic computing and neuromorphic computing are attracting tremendous interests in breaking the memory wall of traditional von Neumann architecture. Photonic memristors equipped with light sensing, data storage, and information processing capabilities are important building blocks of optical neural network. In the recent years, two-dimensional materials (2DMs) have been widely investigated for photonic memristor applications, which offer additional advantages in geometry scaling and distinct applications in terms of wide detectable spectrum range and abundant structural designs. Herein, the recent progress made toward the exploitation of 2DMs beyond graphene for photonic memristors applications are reviewed, as well as their application in photonic synapse and pattern recognition. Different materials and device structures are discussed in terms of their light tuneable memory behavior and underlying resistive switching mechanism. Following the discussion and classification on the device performances and mechanisms, the challenges facing this rapidly progressing research field are discussed, and routes to realize commercially viable 2DMs photonic memristors are proposed. [ABSTRACT FROM AUTHOR]