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Spatial‐temporal time series analysis and forecasting are crucial for understanding dynamic systems and making informed decisions. Recurrent neural networks (RNNs) have paved the way for reservoir computing (RC), a method enabling effective temporal information processing at low training costs. While software‐based RC performs well, physical RC systems face challenges like slow processing speed and limited state richness, leading to high hardware costs. This study introduces an innovative approach, i.e., the antiferroelectric field effect transistor‐based RC (AFeFET‐based RC) system for efficient temporal data processing. By exploiting the fading memory property inherent in hafnium oxide‐based antiferroelectric material, this system demonstrates promise for physical RC implementation. Moreover, it leverages the light sensitivity of 2D molybdenum disulfide (MoS2) channels for controllable temporal dynamics under electrical and optical stimuli. This dual‐mode modulation significantly enriches the reservoir state, boosting overall system performance. Experimental tests on standard benchmarking tasks using the AFeFET‐based RC system yielded impressive accuracy results (95.4%) in spoken‐digit recognition and a remarkable normalized root mean square error (NRMSE) of 0.015 in Mackey–Glass time series prediction. [ABSTRACT FROM AUTHOR]

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]

The development of all‐in‐one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real‐time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet‐of‐Things (IoT) has led to the increasing importance of in‐sensor computing technology, which places computational power at the edge of the data‐flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two‐dimensional (2D) α‐In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate‐tunable linearity of excitatory and inhibitory functions in photon‐induced short‐term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in‐sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre‐programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature‐enhanced scenarios. The findings demonstrate the potential of ferroelectric α‐In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel. [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.

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]

Reservoir computing (RC) architecture which mimics the human brain is a fundamentally preferred method to process dynamical systems that evolve with time. However, the difficulty in generating rich reservoir states using two‐terminal devices remains challenging, which hinders its hardware implementation. Herein, the 1D array of ferroelectric field‐effect transistor (Fe‐FET) based on α‐In2Se3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in α‐In2Se3 is sufficiently investigated by polarization dynamic model. The proposed Fe‐FET is capable of experimentally classifying images using MNIST dataset with a high accuracy of 91%. Furthermore, time‐series real‐life chaotic system, for example, Earth's weather, can be accurately forecasted using our Ferro‐RC based on the Jena climate dataset recorded in a 1 year period. Remarkable determination coefficient (R2) of 0.9983 and normalized root mean square error (NRMSE) of 8.3 × 10−3 are achieved using a minimized readout network. The demonstration of integrated memory and computation opens a route for realizing a compact RC hardware system. [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]

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]

A surface potential based compact model for two-dimensional field effect transistors (2D-FETs) is proposed to incorporate the structural disorders induced transition behaviors among variable range hopping (VRH), nearest neighbor hopping (NNH), and band-like transport in most 2D materials. These functions coupled with effective transport energy and multiple trapping and releasing theory enable our developed model to predict the temperature and carrier density dependent current characteristics. Its validity is confirmed by the experimental results such as the metal insulator transition (MIT) in transition metal dichalcogenides and VRH-NNH transition in black phosphorus nanoribbon. Based on this model, the band-tail effects on the crossover gate voltage of MIT behavior are quantitatively investigated. It is found that the transition behavior is closely related to the distribution of the band-tail states. Furthermore, this model is implemented in Verilog-A for circuit-level prediction and evaluation of 2D-FETs to provide deeper insight into the relationship between material properties, device physics, and circuit performances. [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]

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]

Due to their unique chemical and physical properties, 2D inorganic materials (2DMs) have attracted a lot of attention in recent years as an attractive platform for diverse applications in opto‐electronics, energy generation and storage, and sensing. This article presents the progress being made in sensors designed for measuring volatile organic compounds (VOCs) in ambient air. It considers commercially available sensors, such as the amperometric and conductometric types, and the recent advancements in VOCs sensors employing 2DMs and their nanocomposites. Their performance is summarized in a graphical form and the challenges and possible mitigation methods for these types of VOC sensor are highlighted. [ABSTRACT FROM AUTHOR]

This article presents an analytical method for parameter extraction in oxide semiconductor field-effect transistors (OS FETs), including threshold voltage (VT), effective channel length (Leff), source–drain series resistance (RSD), and intrinsic effective mobility (μint). An analytical expression of effective mobility (μeff) is implemented with the consideration of percolation conduction and RSD effects. This method relies on the concept of Y-function method and the channel length dependent measurement. The extraction results are compared to those from the combination of transfer length method (TLM). It has been shown that a better accuracy of the extracted parameters is achieved with the proposed method. Furthermore, transfer characteristics are resimulated based on the extracted parameters, in which a good match between measurement and simulation is obtained. [ABSTRACT FROM AUTHOR]