ZTO/MgO-Based Optoelectronic Synaptic Memristor for Neuromorphic Computing

Synapse having good linearity plays a vital role in the memory and computing of human brain. Therefore, the achievement of efficient learning process in neuromorphic computing by the implementation of the synaptic functions, such as long-term potentiation/ depression (LTP/LTD) and spike time-dependent plasticity (STDP) of two-terminal optoelectronic memristor device, is critical for the next-generation artificial intelligence. In this work, we improve the resistive switching and synaptic characteristics of a Zn2SnO4 (ZTO)-based optoelectronic synaptic memristor (OSM) by the insertion of an ultrathin MgO layer. For this bilayer (BL) structured OSM, the nonlinearities of LTP and LTD curves are improved to 1.96 and 0.33, respectively. Asymmetrical STDP response demonstrates the suitability of device toward the Hebbian learning. In addition, a Hopfield neural network (HNN) is successfully trained to recognize a 10
$\times10$
pixel input image with an accuracy of
$\sim 100$
% after 15 iterations. Under blue light (405 nm) illumination, OSM emulates the synaptic functions, such as paired pulse facilitation, learning experience behavior, and short- to long-term memory transition. The photoresponse and relaxation characteristics of the device depend on the ionization and neutralization of oxygen vacancies. This highly transparent ZTO/MgO-based OSM with the convergence of “nonvolatile electronic memory and visible light sensor” is suitable as an artificial synapse for neuromorphic computing applications.