Variability-aware electrochemical metallization based memristive neural network for pattern classification

In recent years, memristive neuromorphic systems have gained significant attention. In our previous work, we developed a physics-based framework to model transport in electrochemical metallization (ECM)-based memristors with layered materials as switching layers, which was implemented in VerilogA. In this work, we demonstrate the efficacy of this model in a crossbar array/neural network for pattern classification. The performance of the system is analyzed based on classification accuracy in ideal and non-ideal conditions. Through the use of these simulations, the system-level performance can be predicted, along with its potential degradation, owing to variability.