Dynamic Ferroelectric Transistor-Based Reservoir Computing for Spatiotemporal Information Processing

Abstract

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.