State Characterisation of Self-Directed Channel Memristive Devices

TL;DR

This paper introduces a physics-inspired model and a noise-aware estimation method for characterising and measuring the state of self-directed channel memristors, enhancing their application in memory and neuromorphic systems.

Contribution

It presents a novel modelling and state estimation approach for SDC memristors, improving their reliability for memory and neuromorphic applications.

Findings

Effective physics-inspired model for SDC memristor states

Noise-aware estimation improves state measurement accuracy

Enhanced understanding of memristor dynamics

Abstract

Knowing how to reliably use memristors as information storage devices is crucial not only to their role as emerging memories, but also for their application in neural network acceleration and as components of novel neuromorphic systems. In order to better understand the dynamics of information storage on memristors, it is essential to be able to characterise and measure their state. To this end, in this paper we propose a general, physics-inspired modelling approach for characterising the state of self-directed channel (SDC) memristors. Additionally, to enable the identification of the proposed state from device data, we introduce a noise-aware approach to the minimum-variance estimation of the state from voltage and current pairs.