Multilayer Reservoir Computing Based on Ferroelectric α-In₂ Se₃ for Hierarchical Information Processing.

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-In₂ Se₃ 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.