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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ultralow-power reservoir computing based on bidirectionally operable ferroelectric capacitors with tunable time constants

Minghui Qin, Haipeng Lin, R. Tao, Boyuan Cui et al.
1 citations
Reports on Progress in Physics
Neural Networks and Reservoir Computing
52.10
article

Ultralow-power reservoir computing based on bidirectionally operable ferroelectric capacitors with tunable time constants

Minghui Qin, Haipeng Lin, R. Tao, 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
article en
1 citations

Abstract

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.

Reports on Progress in PhysicsVol. 89(2)
South China Normal University (CN), Collaborative Innovation Center of Advanced Microstructures (CN), Nanjing University (CN)
Affordable and clean energy
Openalex Percentile: Top 1%
Neural Networks and Reservoir Computing
52.10
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.