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Herein, a self-rectifying resistive switching memristor synapse with a Ta/NbOx/Pt structure was demonstrated for deep reservoir computing (RC). The memristor demonstrated stable nonlinear analog switching characteristics, with a rectification ratio of up to 1.6 × 105, good endurance, and high uniformity. Additionally, the memristor exhibited typical short-term plasticity and dynamic synaptic characteristics. Based on these characteristics, a deep memristor RC system was proposed for time series prediction. The system achieved a low normalized root mean square error (NRMSE) of 0.04 in the time series prediction of the Henon map. Even at 90 °C, deep RC retains good predictive power with an NRMSE of only 0.07. This work provides guidance for efficient deep memristive RC networks to handle more complex future temporal tasks. [ABSTRACT FROM AUTHOR]

Physically transient resistive switching devices, a form of memory devices with the ability of achieving physical disappearance in a controllable manner, hold tremendous potentials in multiple security applications. Herein, we demonstrated a physically transient form of memristive device composed of Ag/polyvinyl alcohol/W with configurable resistive switching functionality for security neuromorphic computing. The resistive switching type of the transient device could be modulated effectively by controlling the compliance current during the set process, which was well interpreted by the filament model. Typical synaptic functions pertained to short-term plasticity (STP) and its transition from STP to long-term plasticity were vividly mimicked in this transient memristive device. Importantly, both the synaptic functions and physical form of the transient devices were capable of disappearing instantly upon immersing in de-ionized water, and the dissolution characteristics of the constituent transient materials were investigated experimentally to reveal the degradation mechanism of the device. This transient form of artificial synapse provides foreseeing perspectives on information security enhancement for neuromorphic computing systems. [ABSTRACT FROM AUTHOR]