Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO<sub>2</sub> Memcapacitive Synapse Arrays
Mengjiao Pei, Ying Zhu, Siyao Liu, Hangyuan Cui, Yating Li, Yating Li, Yang Yan, Yun Li, Yun Li, Changjin Wan, Qing Wan
Abstract
Abstract Hardware implementation tailored to requirements in reservoir computing would facilitate lightweight and powerful temporal processing. Capacitive reservoirs would boost power efficiency due to their ultralow static power consumption but have not been experimentally exploited yet. Here, this work reports an oxide‐based memcapacitive synapse (OMC) based on Zr‐doped HfO 2 (HZO) for a power‐efficient and multisensory processing reservoir computing system. The nonlinearity and state richness required for reservoir computing could originate from the capacitively coupled polarization switching and charge trapping of hafnium‐oxide‐based devices. The power consumption (≈113.4 fJ per spike) and temporal processing versatility outperform most resistive reservoirs. This system is verified by common benchmark tasks, and it exhibits high accuracy (>94%) in recognizing multisensory information, including acoustic, electrophysiological, and mechanic modalities. As a proof‐of‐concept, a touchless user interface for virtual shopping based on the OMC‐based reservoir computing system is demonstrated, benefiting from its interference‐robust acoustic and electrophysiological perception. These results shed light on the development of highly power‐efficient human–machine interfaces and machine‐learning platforms.