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monolayer limit, [2,4,7] many of which possess semiconducting properties simultaneously, [8,9] making them promising candidates in memory and neuromorphic computing. [10][11][12][13] Extensively studied, van der Waals semiconducting α-In 2 Se 3 exhibits both IP and OOP ferroelectricity with dipole locking effect, [3,14] facilitating resistive switching in vertical [8,15] and lateral junctions [8,16] as well as a modulation effect from a third terminal. [8,17,18] Ferroelectric semiconductor field-effect transistors (FeSFETs) were also fabricated using non-ferroelectric gate stack and ferroelectric α-In 2 Se 3 as a channel for artificial synapses. [10,12,19] Reservoir computing (RC) is a kind of recurrent neural network (RNN), suited for temporal and sequential information processing. The reservoir computing system is composed of an untrained reservoir to map the input into a high-dimensional space and a trained readout layer for classification. Short-term memory (STM) and nonlinearity are two key features for a reservoir, which can be physically implemented by hardware such as memristive devices. [20] Tremendous progress has been made using different types of memristive devices for pattern classification, [21][22][23][24] spoken-digit recognition, [25][26][27] and time series prediction. [21,25,27] Various forms of nonlinearity have been explored and utilized to realize RC, such as electronic, spintronic, photonic, mechanical, and biological mechanisms. In general, the stability of nonlinearity plays an important role in RC performances, which can be quantitatively evaluated by a local Lyapunov exponent. In particular, a negative local Lyapunov exponent indicates that the system is stable, whereas a positive local Lyapunov exponent indicates chaotic behavior. Previous theoretical studies have revealed that the memory capacity of the reservoir is increased when the system stability is decreased. [28] The memory capacity (MC) is maximized at the edge of chaos and then decreases quickly when the system becomes chaotic. Therefore, a form of nonlinearity that can drive the system to the edge of chaos and hence maximizing its memory capacity is desirable. Based on standard RC, different RC architectures have been proposed in pursuit of more complex dynamics and advanced information functionalities. [28,29] A wide RC is composed of several standard RCs in parallel, which has been actually implemented in previous works to improve network performance. [25,27] On the other hand, deep RC consists of multiple Dynamic physical systems such as reservoir computing (RC) architectures show a great prospect in temporal information processing, whereas hierarchical information processing capability is still lacking due to the absence of advanced multilayer reservoir elements. Here, a stackable reservoir system is constructed based on ferroelectric α-In 2 Se 3 devices with voltage input and output, which is realized by dynamic voltage division between a ferroelectric field-effect transistor and a planar device and therefore allows the...

Reservoir Computing Physically implemented reservoir computing systems have aroused wide interest, due to their capability of efficiently handling temporally complex information. In article number 2108826, Ru Huang, Yuchao Yang, and co‐workers present a multilayer reservoir system using ferroelectric In2Se3 as the key function material, which enables hierarchical information‐processing capability that is suitable for time‐series data processing.

The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy‐efficient artificial‐vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross‐talk issues, and silicon process incompatibility. In this study, a highly linear, light‐tunable, cross‐talk‐free, and silicon‐compatible one‐phototransistor–one‐memristor (1PT1R) optic memristor is experimentally demonstrated for the implementation of an optic artificial neural network (OANN). For optic image recognition in the experiment, an OANN is constructed using a 16 × 3 1PT1R memristor array, and it is trained on an online platform. The model yields an accuracy of 99.3% after only ten training epochs. The 1PT1R memristor, which shows good performance, demonstrates its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing.

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