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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]

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]

UV‐sensing devices have received significant recent attention for applications in areas such as human health, fire detection, and optical communication. One key factor for product commercialization is determining the optimal materials that allow for integration of excellent UV‐sensing properties with compatibility with industrial fabrication processes. However, current UV sensors often fail to achieve this due to either mismatched materials or a device that must be excessively large in order to produce enough photocurrent for UV detection. The UV‐light‐sensing properties of an amorphous InGaZnO4 (IGZO) thin‐film transistor with a dual‐gate structure and relatively small device size (width/length = 50 µm/10 µm) that achieves high sensitivity through a threshold‐voltage‐(Vth)‐adjustment method is proposed. Comparing the drain currents under UV exposure to those under darkened conditions indicates that the ratio between the photoinduced and dark current reaches 106. Furthermore, the UV sensitivity of the dual‐gate transistors can be adjusted by varying the bottom gate voltage, with each pixel of the sensor then being read out separately via scan line pulses. This allows the dual‐gate a‐IGZO transistor to be used for high‐performance UV sensing while being effectively integrated in display applications. [ABSTRACT FROM AUTHOR]

Aluminum Scandium Nitride (Al 1-x Sc x N) has received attention for its exceptional ferroelectric properties, whereas the fundamental mechanism determining its dynamic response and reliability remains elusive. In this work, an unreported nucleation-based polarization switching mechanism in Al 0.7 Sc 0.3 N (AlScN) is unveiled, driven by its intrinsic ferroelectricity rooted in the ionic displacement. Fast polarization switching, characterized by a remarkably low characteristic time of 0.00183 ps, is captured, and effectively simulated using a nucleation-limited switching (NLS) model, where the profound effect of defects on the nucleation and domain propagation is systematically studied. These findings are further integrated into Monte Carlo simulations to unravel the influence of the activation energy for ferroelectric switching on the distributions of switching thresholds. The long-term reliability of devices is also confirmed by time-dependent dielectric breakdown (TDDB) measurements, and the effect of thickness scaling is discussed. Ferroelectric field-effect transistors (FeFETs) are demonstrated through the integration of AlScN and 2D MoS 2 channel, where biological synaptic functions can be emulated with optimized operation voltage. The artificial neural network built from AlScN-based FeFETs achieves 93.8% recognition accuracy of handwritten digits, demonstrating the potential of ferroelectric AlScN in future neuromorphic computing applications., (© 2024 Wiley‐VCH GmbH.)