State Characterisation of Self-Directed Channel Memristive Devices

2025-05-21

AI Summary

Addressing the challenge of accurately characterizing and dynamically monitoring the internal state of spintronic domain-wall memristors (SDCs), this paper proposes a physics-informed, general-purpose modeling and noise-robust estimation framework. First, we establish the first physically consistent and interpretable state evolution model for SDC memristors, grounded in fundamental spintronic mechanisms. Second, we design a noise-aware minimum-variance state estimator that directly reconstructs the internal state from time-varying voltage–current measurements—overcoming the limitations of conventional static I–V curve fitting. The framework enables reproducible, quantitative, and real-time state characterization. Experimental validation demonstrates a 42% reduction in state estimation error compared to standard approaches, significantly enhancing monitoring reliability. This work provides a foundational tool for online state awareness and reliability assessment in neuromorphic hardware systems.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Characterize state of self-directed channel memristors
• Model memristor dynamics for information storage
• Estimate state from voltage-current pairs with noise

Innovation

• Physics-inspired modeling for SDC memristor state
• Noise-aware minimum-variance estimation technique
• Voltage-current pairs for state identification