Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks

Abstract

Neuromorphic computation based on resistive switching devices represents a relevant hardware alternative for artificial deep neural networks. For the highest accuracies on pattern recognition tasks, an analog, linear, and symmetric synaptic weight is essential. Moreover, the resistive switching devices should be integrated with the supporting electronics, such as thin-film transistors (TFTs), to solve crosstalk issues on the crossbar arrays. Here, an a-Indium-gallium-zinc-oxide (IGZO) memristor is proposed, with Mo and Ti/Mo as bottom and top contacts, with forming-free analog switching ability for an upcoming integration on crossbar arrays with a-IGZO TFTs for neuromorphic hardware systems. The development of a TFT compatible fabrication process is accomplished, which results in an a-IGZO memristor with a high stability and low cycle-to-cycle variability. The synaptic behavior through potentiation and depression tests using an identical spiking scheme is presented, and the modulation of the plasticity characteristics by applying non-identical spiking schemes is also demonstrated. The pattern recognition accuracy, using MNIST handwritten digits dataset, reveals a maximum of 91.82% accuracy, which is a promising result for crossbar implementation. The results displayed here reveal the potential of Mo/a-IGZO/Ti/Mo memristors for neuromorphic hardware.