Low-Power Switching of Memristors Exhibiting Fractional-Order Dynamics

In this conference contribution, we present some initial results on switching memristive devices exhibiting fractional-order behavior using current pulses. In our model, it is assumed that the evolution of a state variable follows a fractional-order differential equation involving a Caputo-type derivative. A study of Joule losses demonstrates that the best switching strategy minimizing these losses depends on the fractional derivative's order and the power exponent in the equation of motion. It is found that when the order of the fractional derivative exceeds half of the power exponent, the best approach is to employ a wide pulse. Conversely, when this condition is not met, Joule losses are minimized by applying a zero current followed by a narrow current pulse of the highest allowable amplitude. These findings are explored further in the context of multi-pulse control. Our research lays the foundation for the advancement of the next generation of energy-efficient neuromorphic computing architectures that more closely mimic their biological counterparts.

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Publication Details

Published
2025-10-22
DOI
https://doi.org/10.1109/metroxraine66377.2025.11340470
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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Low-Power Switching of Memristors Exhibiting Fractional-Order Dynamics

Yuriy V. Pershin, Nathan Astin
1 citations
Advanced Memory and Neural Computing
0.73
article

Low-Power Switching of Memristors Exhibiting Fractional-Order Dynamics

Yuriy V. Pershin, Nathan Astin
article en
1 citations

Abstract

In this conference contribution, we present some initial results on switching memristive devices exhibiting fractional-order behavior using current pulses. In our model, it is assumed that the evolution of a state variable follows a fractional-order differential equation involving a Caputo-type derivative. A study of Joule losses demonstrates that the best switching strategy minimizing these losses depends on the fractional derivative's order and the power exponent in the equation of motion. It is found that when the order of the fractional derivative exceeds half of the power exponent, the best approach is to employ a wide pulse. Conversely, when this condition is not met, Joule losses are minimized by applying a zero current followed by a narrow current pulse of the highest allowable amplitude. These findings are explored further in the context of multi-pulse control. Our research lays the foundation for the advancement of the next generation of energy-efficient neuromorphic computing architectures that more closely mimic their biological counterparts.

University of South Carolina (US)
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
0.73
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