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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]

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.

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 this article, a β-Ga2O3 metal-interlayer-semiconductor Schottky barrier diode (MIS-SBD) using Al-reacted aluminum oxide as the interlayer is demonstrated for the first time and compared with conventional metal-semiconductor (MS) Schottky barrier diode (SBD). The aluminum oxide is formed by sputtering a thin Al layer on Ga2O3 substrate and then annealed in O2 at 300 °C. With the insertion of Al-reacted interlayer, the SBD subthreshold swing (SS) is significantly improved to 61 mV/dec with an average current range of > 6 orders. Example of atomic layer deposited (ALD) Al2O3 as the interlayer is also fabricated and characterized. J – V study corrected by Gaussian distribution model shows that all the samples statistically exhibit similar mean barrier heights (BHs). This indicates that the interlayer hardly affects the electrostatic field and band bending as experienced by carrier injections. C – V study provides different BH results in different sample setups. The result proves that Al-reacted interfacial layer helps eliminate interface degradation as compared with ALD Al2O3. Overall, MIS-SBD by Al-reaction method exhibits improved SS, reduced reverse leakage current (I0), low ideality factor, good ON-OFF ratio (> 109), and minimized interface charges as compared with its respective counterparts. The result of this article serves as a promising interface engineering technique for Ga2O3-based SBD designs. [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]

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]