Reliability of Memristive Devices for High-Performance Neuromorphic Computing: (Invited Paper)

Abstract

With the rich internal ion dynamics, memristor-based neuromorphic computing emerges as a non-von Neumann computing paradigm to mimic biological neural networks and achieve high energy efficiency. However, to implement large-scale memristive neural networks, the reliability issue of memristive devices, including artificial synapse, dendrite, and soma, should be properly addressed. In this paper, recent works investigating the physical mechanisms and optimizations of memristive device reliability are presented. In particular, the relaxation effect of $\boldsymbol{\text{HfO}_{\mathrm{x}}}$ -based artificial synapse is alleviated by using a ternary oxide as the thermal enhance layer, the device yield of $\boldsymbol{\text{TiO}_{\mathrm{x}^{-}}}$ based artificial dendrite is improved by proper material selection and interface engineering, and the device variability of $\boldsymbol{\text{NbO}_{\mathrm{x}}}$ -based artificial soma is reduced by nitrogen doping. Furthermore, a bio-inspired dendritic neural network with these three fundamental memristive devices is constructed and simulated to analyze the influence of device reliability. Using these optimized devices, the classification accuracy of the street-view house number dataset can be improved by up to $\sim$ 60%. The quantitative requirements of device reliability metrics are also provided as a guideline for future neuromorphic system design and implementation.