Impact of inert electrode on the volatility and non-volatility switching behavior of SiO2-based conductive bridge random access memory devices

The development of disruptive artificial neural networks (ANNs) endowed with brain-inspired neuromorphic capabilities is emerging as a promising solution to deal with the challenges of the artificial intelligence era. The fabrication of robust and accurate ANNs is strongly associated with the design of new electronic devices. The intriguing properties of memristors render them suitable as building blocks within ANNs. However, the impact of the operating electrodes on the dynamics of the switching process and the relaxation effect remains elusive. It is, thus, apparent that a deep understanding of the underlying electrochemical metallization mechanism that affects the formation of the conductive filament is of great importance. Along these lines, in this work, the impact of various materials as inert electrodes (Pt NPs, ITO, n++ Si, TiN, and W) on tuning the switching mode of low power SiO2-based conductive bridge random access memory devices was systematically investigated. A comprehensive model was applied to interpret the threshold and bipolar switching patterns and shed light on the respective physical mechanisms. The model incorporated the different coefficients of thermal conductivity of the various materials and attempted to associate them with the Soret coefficient and the activation energy of thermophoresis to interpret the experimental outcomes. Our work provides valuable insight for the realization of memristive devices with tunable properties, which can be directly leveraged for implementing a variety of neuromorphic functionalities, such as synaptic plasticity and spike generation.