Physical reservoir computing (PRC) exploits intrinsic material dynamics for energy-efficient neuromorphic hardware. However, conventional resistive PRC implementations suffer from high power consumption and hardware complexity owing to leakage currents and the requirement for peripheral readout circuitry. Here, we demonstrate an electric-double-layer memcapacitive reservoir based on an Al/nanogranular SiO2/ITO sandwiched structure. The device enables direct open-circuit voltage readout and utilizes reversible ionic charge accumulation at the interface, enabling robust nonlinearity and short-term memory. The PRC system based on such a device achieves a normalized root mean square error of 0.097 in the Mackey–Glass chaotic time series prediction task. It also yields high human action recognition accuracies of 92.00% on the profile-based Weizmann dataset (10 classes) and 86.54% on the depth-based UTD multimodal human action dataset (27 classes), respectively. This memcapacitive device shows a single-pulse energy consumption of 0.68 pJ and an operating power of ∼27 pW in action recognition task, superior to most traditional current-readout reservoirs by over an order of magnitude. These results highlight a structurally simple and highly energy-efficient physical pathway for neuromorphic visual processing.
Cui et al. (Mon,) studied this question.