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

作者：Linyuan Mo, Zhen Fan, Jiali Ou, Zhiwei Chen, Haipeng Lin, Wenjie Hu, Wenjie Li, Meixia Li, Boyuan Cui, Hua Fan, Ruiqiang Tao, Guo Tian, Minghui Qin, Xubing Lu, Guofu Zhou, Xingsen Gao, Junming Liu · 发表于：Reports on Progress in Physics · 年份：2026 · DOI：10.1088/1361-6633/ae3984 · 被引用次数：3 · 研究领域：Neural Networks and Reservoir Computing、Ferroelectric and Negative Capacitance Devices、Advanced Memory and Neural Computing

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, d...