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 (R 2) 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.

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.

Unfortunately, the substantial power consumption has appeared to be a non-trivial burden on the training protocol of ANNs, making their feasibility challenging, especially on edge devices. One primary reason is that ANN processes information continuously with no temporal resolution, resulting in redundant power usage. Indeed, excessive computational operations in the current ANNs can barely mimic the biological behavior.On the other hand, as inspired by biological systems, the spiking neural network (SNN) has attracted ever-growing interest as the network communicates and transmits information using discrete spikes. [7][8][9][10][11][12] In biological systems, the sensory periphery receives stimulation from the surroundings and converts the analog stimuli to spikes that are subsequently relayed to the brain through sophisticated neuron connections, as shown in Figure 1a. The biological neurons collect the input spikes from other neurons, and the output spikes are generated once the membrane potential exceeds the threshold. The SNNs are believed to best represent their biological counterparts where the leaky-integrate-fire (LiF) model has been frequently implemented. [13,14] Several demonstrations have confirmed its competitiveness in various machine learning applications while consuming significantly lower energy than conventional ANNs. [7][8][9][10][11][12] Nevertheless, a biomimetic encoder is needed for converting the external stimuli, a continuous variable, to a spiking format before relaying the information to the neural network. It is crucial that the encoder must not deteriorate the performance of SNN, in which the encoding resolution, conversion speed, power consumption, and noise resilience are the essential metrics for evaluation. Although hardware encoders, including complementary metal-oxide-semiconductor (CMOS), memristors, and transistors, have been reported, the challenges remain as the encoding procedure is either sophisticated (e.g., digital circuit implementation) or power inefficient (e.g., low throughput). [15][16][17][18][19][20] Thus, this imposes stringent criteria on the fundamental level where innovative material solutions are practically indispensable. The hafnium oxide-based ferroelectric material system offers exceptional opportunities from the aforementioned aspects. We propose that the randomly distributed Spiking neural network (SNN), where the information is evaluated recurrently through spikes, has manifested significant promises to minimize the energy expenditure in data-intensive machine learning and artificial intelligence. Among these applications, the artificial neural encoders are essential to convert the external stimuli to a spiking format that can be subsequently fed to the neural network. Here, a molybdenum disulfide (MoS 2 ) hafnium oxide-based ferroelectric encoder is demonstrated for temporal-efficient information processing in SNN. The fast domain switching attribute associated with the polycrystalline nature of hafnium oxide-based ferroelectric material is exploited...

Internet‐of‐Things (IoT) is a ubiquitous network that features a tremendous amount of data and myriads of heterogeneous devices, which are interconnected and accessible or controllable anywhere and anytime. The security of IoT is therefore unequivocally crucial in several aspects, such as device‐to‐device communication, sensing and actuating, and information exchange. Conventional cryptographic algorithms and silicon‐based security primitives are constantly challenged by evolving methods of attack. By far, many efforts and achievements have been made using 2D materials for various electronics applications. Therefore, it is plausible to explore the implementation of hardware security using 2D materials, for example, true random number generators (TRNGs), physical unclonable functions (PUFs), camouflage, and anticounterfeit. TRNGs and PUFs are critical elements of hardware security and are widely deployed in cryptographic keys, identification, and authentication. In contrast to conventional utilization of manufacturing variations, security primitives using 2D materials have other entropy sources to exploit, such as the random nature of material growth and intrinsic randomness in charge trapping/detrapping. In this review, research progresses in 2D material‐based TRNGs, PUFs, and other security applications are summarized, along with the discussion on entropy sources, reliability, circuit, and machine learning modeling attacks launched on TRNGs and PUFs.

By harnessing the physically unclonable properties, true random number generators (TRNGs) offer significant promises to alleviate security concerns by generating random bitstreams that are cryptographically secured. However, fundamental challenges remain as conventional hardware often requires complex circuitry design, showing a predictable pattern that is susceptible to machine learning attacks. Here, a low‐power self‐corrected TRNG is presented by exploiting the stochastic ferroelectric switching and charge trapping in molybdenum disulfide (MoS2) ferroelectric field‐effect transistors (Fe‐FET) based on hafnium oxide complex. The proposed TRNG exhibits enhanced stochastic variability with near‐ideal entropy of ≈1.0, Hamming distance of ≈50%, independent autocorrelation function, and reliable endurance cycle against temperature variations. Furthermore, its unpredictable feature is systematically examined by machine learning attacks, namely the predictive regression model and the long‐short‐term‐memory (LSTM) approach, where nondeterministic predictions can be concluded. Moreover, the generated cryptographic keys from the circuitry successfully pass the National Institute of Standards and Technology (NIST) 800–20 statistical test suite. The potential of integrating ferroelectric and 2D materials is highlighted for advanced data encryption, offering a novel alternative to generate truly random numbers.