Ultralow-power reservoir computing based on bidirectionally operable ferroelectric capacitors with tunable time constants
Physical reservoir computing (RC) systems have emerged as a prominent research frontier due to their exceptional efficiency in temporal information processing. However, existing implementations, predominantly utilizing resistive devices, face challenges pertaining to power efficiency and dynamic richness. Here, we propose a ferroelectric capacitor-linear capacitor (FC-LC) series device for RC implementation. By leveraging nonlinear polarization switching and back-switching, the FC-LC series device realizes two essential reservoir properties: nonlinearity and fading memory. In addition, the device exhibits an ultralow power consumption, which, along with its direct voltage readout capability, marks a significant advance over resistive reservoir devices. Moreover, the device features bidirectional operation and widely tunable time constants, thereby enhancing reservoir space dimensionality and state richness. Building upon these FC-LC series devices, a ferroelectric capacitive RC system is developed, which demonstrates superior performance in various benchmark tasks. By exploiting the bidirectional operation of the device, the RC system not only delivers enhanced performance in waveform classification but also enables high-accuracy multimodal digit recognition. Through strategically hybridizing the FC-LC series devices with varying time constants, the RC system achieves remarkable performance in Mackey-Glass time-series prediction. Our study paves the way for power-efficient, dynamic-rich RC systems capable of handling diverse temporal tasks.
Authors
- Minghui Qin (ORCID: https://orcid.org/0000-0002-8306-125X)
- Haipeng Lin
- R. Tao (ORCID: https://orcid.org/0009-0001-6249-9180)
- Boyuan Cui
- Jiali Ou
- Linyuan Mo
- Guo Tian
- Xingsen Gao
- Wenjie Li
- Wenjie Hu
- Junming Liu
- Guofu Zhou
- Zhen Fan
- Meixia Li
- Zhiwei Chen
- Hua Fan
- Xubing Lu
Institutions
- South China Normal University (CN)
- Collaborative Innovation Center of Advanced Microstructures (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Reports on Progress in Physics
- Published
- 2026-01-16
- DOI
- https://doi.org/10.1088/1361-6633/ae3984
- Citations
- 1
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 52.10