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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]

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]

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]

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]