Bio-realistic synaptic characteristics in the cone-shaped ZnO memristive device

Abstract

We demonstrate inherent biorealistic synaptic plasticity functions in the Pt/n-ZnO/SiO 2-x /Pt heterostructures, where the n-ZnO semiconductor is geometrically cone-shaped in the size of a few nanometers. The synaptic functions were achieved within a two-terminal, electroforming-free, and low-power rectifying diode-like resistive switching device. The important rate-dependent synaptic functions, such as the nonlinear transient conduction behavior, short-and long-term plasticity, paired-pulse facilitation, spike-rate-dependent plasticity and sliding threshold effect, were investigated in a single device. These characteristics closely mimic the memory and learning functions of those in biosynapses, where frequency-dependent identical spiking operations are mostly taking place, and we emulate these characteristics in the "Learning-Forgetting-Relearning" synaptic behavior. The switching dynamics in the cone-shaped n-ZnO semiconductor are correlated with the transport mechanism along the grain boundaries of the charged ion species, namely, oxygen vacancies and charged oxygen. The diffusion and generation/recombination of these defects have specific time scales of self-decay by virtue of the asymmetric profile of the n-ZnO cone defects. Finally, the essential biorealistic synaptic plasticity functions were discovered for the perspectives of dynamic/adaptive electronic synapse implementations in hardware-based neuromorphic computing.

Memristive Synapses and Neurons for Bioinspired Computing

“…Wang and coworkers [198] observed the learning experience-dependent plasticity in amorphous InGaZnO memristive devices. Sokolov et al [157] also observed a similar phenomenon in the cone-shaped ZnO memristive device, where the forgetting process was carried out by applying low-frequency stimuli. Then, the synaptic weight relaxes to an intermediate state during the forgetting process after removing the stimulus (see Figure 13a).…”

“…www.advelectronicmat.de be utilized to mimic LTP with forgetting effect, [159,[161][162][163] STP to LTP transformation, [157,160,[164][165][166][167][168][169][170] metaplasticitry, [112,[171][172][173][174][175] and triplet-STDP. [176][177][178] The device structure, size, switching performance, and emulated synaptic plasticity of the representative memristive devices are summarized in Table 1.…”

“…A similar approach has been adopted to biorealistically implement STDP learning rules in the biomolecular memristive devices. [157] Very recently, Xiong et al [163] realized the tunable forgetting rate by engineering the electrode/oxide interface by controlling the electrode composition in STO memristive devices, as reported in Figure 10d,e. Lu and his coworkers demonstrated BCM rules in Pd/WO x /W second-order memristive devices.…”

“…Mater. [157] Copyright 2019, The Authors, published by Springer Nature. Implementation of suppression triplet-STDP in second-order memristive devices.…”

Recent Progress in Synaptic Devices Based on 2D Materials

“…Various synaptic devices based on resistive switching, driven by different physical working mechanisms such as active metallic filament, charge trapping/detrapping effect, ions/vacancies migration, phase change behaviors, ferroelectric polarization, and spin‐transfer torque‐based synapses, have been demonstrated for emerging memory and neuromorphic computing. Many scientists are actively working to resolve various issues in those synaptic devices: high energy consumption, low switching speed, poor reliability, or the lack of high device density for integration.…”