Nitrogen doping effect on InGaZnO-based artificial synapse for implementing reservoir computing and SVHN dataset pattern recognition

Abstract

This work focused on plasma-induced nitrogen-doped indium gallium zinc oxide (InGaZnO:N) based resistive switching devices. Nitrogen atoms in the InGaZnO:N can reduce the randomness of oxygen vacancy formation and conducting filaments, resulting in stable and uniform resistive switching (RS) and artificial synaptic performance. Optimized nitrogen and oxygen ion concentration improves the redox reaction depending on their reactivity with InGaZnO due to optimal VO concentration in the switching layer confirmed by X-ray photoelectron spectroscopic (XPS) analysis. Device-to-device promising RS variability with ION/IOFF ratio >10 confirmed with multiple devices with low average SET/RESET voltages of 1.35 /-1.5 V. Pulse-induced shortterm memory learning characteristics with linear weight-update, including synaptic function of short-term potentiation (STP), paired-pulse facilitation (PPF), spike rate-dependent plasticity (SRDP), and experience dependent synaptic weight modification are successfully emulated and demonstrated. The optimization of InGaZnO:N-based memristors provides efficient temporal information processing capability, serving as the physical reservoir computing system, demonstrated through experimental pattern recognition. The potentiaiton/ depression of conductance was used for on-chip learning with the street view house numbers (SVHN) dataset. This study suggests that moderate nitrogen-doped ITO/InGaZnO:N/ITO memristors possess potential as synaptic devices for neuromorphic systems.