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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.

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]

In‐memory computing, particularly neuromorphic computing, has emerged as a promising solution to overcome the energy and time‐consuming challenges associated with the von Neumann architecture. The ferroelectric field‐effect transistor (FeFET) technology, with its fast and energy‐efficient switching and nonvolatile memory, is a potential candidate for enabling both computing and memory within a single transistor. In this study, the capabilities of an integrated ferroelectric HfO2 and 2D MoS2 channel FeFET in achieving high‐performance 4‐bit per cell memory with low variation and power consumption synapses, while retaining the ability to implement diverse learning rules, are demonstrated. Notably, this device accurately recognizes MNIST handwritten digits with over 94% accuracy using online training mode. These results highlight the potential of FeFET‐based in‐memory computing for future 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]

The analog resistive switching (RS) characteristics in 2-D polycrystalline (poly-) molybdenum disulfide (MoS2) field-effect transistors (FETs) enable new electronic devices capable of emulating biological synaptic behaviors. In 2-D poly-materials, grain boundary (GB)-induced trap states are of major significance to RS behaviors. However, there is still a lack of appropriate compact models that capture accurate physical mechanisms. Therefore, we developed a surface potential-based compact model, based on the theories of the GB energy barrier and space charge limited current (SCLC). By calibrating to experimental data of MoS2, the physical parameters are extracted, and the model explains the scaling behaviors of channel lengths and grain sizes. Due to the electric-field-induced defect redistribution, the energy barrier modulation at a single-GB (e.g., intersecting GB) quantitatively matches the reported experiments. Moreover, the possible SCLC-based RS behavior is also investigated. Furthermore, we have optimized the set/reset process and simulated the postsynaptic current (PSC) with a tunable potentiation (or depression) process, and then the gate voltage dependence and statistical effects on RS and PSC have been investigated. Thus, this model provides important devices physics insights of 2-D poly-materials and it guides device design, fabrication, and material engineering, to meet the requirements of the future neuromorphic computing application. [ABSTRACT FROM AUTHOR]

Neuromorphic computing, which emulates the biological neural systems could overcome the high‐power consumption issue of conventional von‐Neumann computing. State‐of‐the‐art artificial synapses made of two‐terminal memristors, however, show variability in filament formation and limited capacity due to their inherent single presynaptic input design. Here, a memtransistor‐based artificial synapse is realized by integrating a memristor and selector transistor into a multiterminal device using monolayer polycrys‐talline‐MoS2 grown by a scalable chemical vapor deposition (CVD) process. Notably, the memtransistor offers both drain‐ and gate‐tunable nonvolatile memory functions, which efficiently emulates the long‐term potentiation/depression, spike‐amplitude, and spike‐timing‐dependent plasticity of biological synapses. Moreover, the gate tunability function that is not achievable in two‐terminal memristors, enables significant bipolar resistive states switching up to four orders‐of‐magnitude and high cycling endurance. First‐principles calculations reveal a new resistive switching mechanism driven by the diffusion of double sulfur vacancy perpendicular to the MoS2 grain boundary, leading to a conducting switching path without the need for a filament forming process. The seamless integration of multiterminal memtransistors may offer another degree‐of‐freedom to tune the synaptic plasticity by a third gate terminal for enabling complex neuromorphic learning. [ABSTRACT FROM AUTHOR]