Journal Article

All-ferroelectric implementation of reservoir computing

Chen, Zhiwei, Lu, Xubing, Liu, Jun-Ming, Gao, Xingsen, Li, Wenjie, Fan, Zhen, Zhou, Guofu, Dong, Shuai, Qin, Minghui, Zeng, Min, Chen, Yihong

2023

Overview

Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO 3 /SrRuO 3 structure via the manipulation of an imprint field ( E imp ). It is shown that the volatile FD with E imp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible E imp displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing. While reservoir computing can process temporal information efficiently, its hardware implementation remains a challenge due to the lack of robust and energy efficient hardware. Here, the authors develop an all-ferroelectric reservoir computing system, showing high accuracies and low power consumptions in various tasks like the time-series prediction.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/301/1005/1008 / 639/301/119/996 / Cognition / Data processing / Energy efficiency / Ferroelectric materials / Ferroelectricity / Hardware / Humanities and Social Sciences / Information processing / Long-Term Potentiation / Memory, Short-Term / Memristors / multidisciplinary / Neuronal Plasticity / Nonlinear systems / Nutritional Status / Power consumption / Science / Science (multidisciplinary) / Short term memory / Time series