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Two‐dimensional Materials (2DMs) offer significant promise for advancing device miniaturization and extending Moore's law. Despite the challenges posed by high contact resistance in transistors, recent discoveries highlight semimetals as an effective approach for achieving ohmic contact with near‐quantum‐limit contact resistance. The energy band hybridization between semimetal and MoS2 is found to create degenerate states and heavily doped contact, which is proposed as the underlying mechanism responsible for reducing contact resistance. However, a quantitative and comprehensive characterization of the semimetal‐MoS2 interface is lacking, leaving the physical interactions elusive. This study reveals that semimetals induce n‐type doping and tensile strain in monolayer MoS2 grown using CVD, which serve as the contact resistance and mobility boosters. Among the semimetals investigated, including Bismuth (Bi), Antimony (Sb), and their alloy, Bi results in the highest electron doping of 2 × 1013 cm−2 and a 0.5% tensile strain, leading to reduced contact resistance and enhanced mobility. First‐principles calculations and spectroscopy measurements unveil the impact of electron doping and strain in MoS2, and the thermal effects are subsequently explored. This research underscores the potential of semimetals in boosting device performance and lays the foundation for reducing contact resistance in transistors made from 2D materials. [ABSTRACT FROM AUTHOR]

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]

Time‐series analysis and forecasting play a vital role in the fields of economics and engineering. Neuromorphic computing, particularly recurrent neural networks (RNNs), has emerged as an effective approach to address these tasks. Reservoir computing (RC), a type of RNN, offers a powerful and efficient solution for handling nonlinear information in high‐dimensional spaces and addressing temporal tasks. CuInP2S6 (CIPS), a van der Waals material with ion conductivity, shows promise for sequential task processing. Here, a synapse device based on CIPS is demonstrated that exhibits temporal dynamics under electrical stimulation. By controlling Cu+ ion migration, this study successfully emulates synaptic performance, including potentiation and depression characteristics, and RC. Migration of Cu+ ions is confirmed using piezoresponse and Kelvin probe force microscopy. The device achieves low normalized root mean square errors (NRMSE) of 0.04762 and 0.01402 for the Hénon map and Mackey‐Glass series tasks, respectively. For real‐life time‐series prediction based on the Jena temperature database, an overall NRMSE of 0.03339 is achieved. These results highlight the potential of CIPS ion conductivity for real‐time signal processing in machine learning, expanding applications in neuromorphic computing. [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]

Artificial synapses in neuromorphic computing systems hold potential to emulate biological synaptic plasticity to achieve brain-like computation and autonomous learning behaviors in non-von-Neumann systems. 2D materials, such as graphene, graphene oxide, hexagonal boron nitride, transition metal dichalcogenides, transition metal oxides, 2D perovskite, and black phosphorous, have been explored to achieve many functionalities of biological synapses due to their unique electronic, optoelectronic, electrochemical, and mechanical properties that are lacking in bulk materials. This review features the current development in the state-of-the-art artificial synaptic electronic devices based on 2D materials. The structures of these devices are first discussed according to their number of terminals (two-, three-, four-, and multi-terminals) and geometric layouts (vertical, horizontal, hybrid). Since different 2D materials have been utilized to fabricate these devices, their underlying physical mechanisms and principles are further discussed, and their artificial neuron synaptic functionalities and performances are analyzed and contrasted. Finally, a summary of the current research status and major achievements is concluded, and the outlooks and perspectives for this emerging and vibrant field and the potential applications of these devices for neuromorphic computing are presented. [ABSTRACT FROM AUTHOR]

2D semiconductor field‐effect transistors (2D FETs) have emerged as a promising candidate for beyond‐silicon electronics applications. However, its device performance has often been limited by the metal‐2D semiconductor contact, and the non‐negligible contact resistance (RSD) not only deteriorates the on‐state current but also hinders the direct characterization of the intrinsic properties of 2D semiconductors (e.g., intrinsic charge carrier mobility, μint). Therefore, a proper extraction technique that can independently characterize the metal‐2D semiconductor contact behavior and the intrinsic properties of a 2D semiconducting layer is highly desired. In this study, a universal yet simple method is developed to accurately extract the critical parameters in 2D FETs, including characteristic temperature (To), threshold voltage (VT), RSD, and μint. The practicability of this method is extensively explored by characterizing the temperature‐dependent carrier transport behavior and the strain‐induced band structure modification in 2D semiconductors. Technology computer aided design simulation is subsequently employed to verify the precision of RSD extraction. Furthermore, the universality of the proposed method is validated by successfully implementing the extraction to various 2D semiconductors, including black phosphorus, indium selenide, molybdenum disulfide, rhenium disulfide, and tungsten disulfide with top‐ and bottom‐gated configurations. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-22913-7, published online 26 October 2022 In the original version of this Article, Kah-Wee Ang and Yong-Wei Zhang were omitted as corresponding authors. Correspondence and requests for materials should also be addressed to kahwee.ang@nus.edu.sg and zhangyw@ihpc.a-star.edu.sg The original Article has been corrected. [Extracted from the article]