Multilayer Reservoir Computing Based on Ferroelectric α‐In<sub>2</sub>Se<sub>3</sub> for Hierarchical Information Processing
Keqin Liu, Bingjie Dang, Teng Zhang, Zhen Yang, Lin Bao, Liying Xu, Caidie Cheng, Ru Huang, Yuchao Yang
Abstract
Abstract 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 reservoirs to cascade, enabling multilayer RC. Fast Fourier transformation analysis shows high‐harmonic generation in the first layer as a result of inherent nonlinearity of the reservoir, and progressive low‐pass filtering effect is realized where higher‐frequency components are progressively filtered in deeper‐layer RCs. Time‐series prediction and waveform classification tasks are also demonstrated, serving as evidence for the memory capacity and computing capability of the deep reservoir architecture. This work can provide a promising pathway in exploiting emerging 2D materials and dynamics for advanced neuromorphic computing architectures.