An energy efficient reservoir computing system based on HZO memcapacitive devices

Abstract

Memcapacitor devices based on ferroelectric material have attracted attention recently in application of neuromorphic computing due to lower static power relative to memristors. They have been used for establishing fully connected neural networks but not yet for recurrent neural networks (RNNs), which owns the advantage in temporal signal processing. As an improved network architecture for RNNs, reservoir computing (RC) is easier to train and energy efficient. In this work, an HZO-based ferroelectric memcapacitor is used as the reservoir layer to recognize handwritten digits. A recognition accuracy of 90.3% is achieved. Meanwhile, a task of predicting Mackey–Glass time series is built to demonstrate the advantage of reservoir networks in processing time-series signals. A normalized root mean square error of 0.13 was obtained, indicating that this system can predict the Mackey–Glass chaotic system well. In addition, the energy consumption in the input signal and recognition task is significantly lowered compared with a memristor-based network. Our work provides an energy efficient way to build up the RC network.