A free energy landscape analysis of resistance fluctuations in a memristive device

Resistance noise in memristive devices is often attributed to simple thermally activated processes, such as fluctuations across single energy barriers. However, this picture may underestimate the complexity of the underlying atomic dynamics, which can be described as transitions between many local minima in a high-dimensional free energy landscape shaped by energetic and entropic contributions, yet such landscapes are difficult to access experimentally. Using a hidden Markov model, we analyse resistance fluctuations in a nanoscopic volume of the phase-change material germanium telluride. We quantify the transition rates between discrete resistance states over a wide temperature range. The rates follow an Arrhenius-like behaviour, but the extracted attempt frequencies span several orders of magnitude and include values far below typical phonon frequencies. This spread reflects substantial entropic contributions to the free energy barriers, which we quantify by tracking individual transitions across temperatures. This approach should be broadly applicable to memristive materials, where significant resistance changes are linked to atomic-scale transitions.

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Publication Details

Journal
Nature Materials
Published
2026-01-30
DOI
https://doi.org/10.1038/s41563-026-02487-9
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
21.93
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article

A free energy landscape analysis of resistance fluctuations in a memristive device

Sebastian Walfort, Martin Salinga, Nils Holle, Niklas Vollmar et al.
1 citations
Nature Materials
Advanced Memory and Neural Computing
21.93
article

A free energy landscape analysis of resistance fluctuations in a memristive device

Sebastian Walfort, Martin Salinga, Nils Holle, Niklas Vollmar, Xuân Thắng Vũ, Jakob Ballmaier
article en
1 citations

Abstract

Resistance noise in memristive devices is often attributed to simple thermally activated processes, such as fluctuations across single energy barriers. However, this picture may underestimate the complexity of the underlying atomic dynamics, which can be described as transitions between many local minima in a high-dimensional free energy landscape shaped by energetic and entropic contributions, yet such landscapes are difficult to access experimentally. Using a hidden Markov model, we analyse resistance fluctuations in a nanoscopic volume of the phase-change material germanium telluride. We quantify the transition rates between discrete resistance states over a wide temperature range. The rates follow an Arrhenius-like behaviour, but the extracted attempt frequencies span several orders of magnitude and include values far below typical phonon frequencies. This spread reflects substantial entropic contributions to the free energy barriers, which we quantify by tracking individual transitions across temperatures. This approach should be broadly applicable to memristive materials, where significant resistance changes are linked to atomic-scale transitions.

Nature Materials
University of Münster (DE), University Hospital Münster (DE), RWTH Aachen University (DE)
Affordable and clean energy
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
21.93
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A free energy landscape analysis of resistance fluctuations in a memristive device — Sebastian Walfort, Martin Salinga, et al. · Nature Materials (2026) | TGRS Research Map | TGRS