Filamentary Switching: Synaptic Plasticity through Device Volatility

TL;DR

This paper demonstrates that filamentary switching in electrochemical memristive devices can emulate biological synaptic plasticity, with complex filament shapes enabling independent control of short- and long-term memory processes, advancing neuromorphic hardware development.

Contribution

It introduces a novel approach using filament shape complexity to independently control synaptic plasticity phases in memristive devices, bridging device physics and biological function.

Findings

Filamentary switching can reproduce key biological synaptic functions.

Complex filament shapes enable independent control of plasticity phases.

The device's features are promising for neuromorphic hardware applications.

Abstract

Replicating the computational functionalities and performances of the brain remains one of the biggest challenges for the future of information and communication technologies. Such an ambitious goal requires research efforts from the architecture level to the basic device level (i.e., investigating the opportunities offered by emerging nanotechnologies to build such systems). Nanodevices, or, more precisely, memory or memristive devices, have been proposed for the implementation of synaptic functions, offering the required features and integration in a single component. In this paper, we demonstrate that the basic physics involved in the filamentary switching of electrochemical metallization cells can reproduce important biological synaptic functions that are key mechanisms for information processing and storage. The transition from short- to long-term plasticity has been reported as a direct consequence of filament growth (i.e., increased conductance) in filamentary memory devices. In this paper, we show that a more complex filament shape, such as dendritic paths of variable density and width, can permit the short- and long-term processes to be controlled independently. Our solid-state device is strongly analogous to biological synapses, as indicated by the interpretation of the results from the framework of a phenomenological model developed for biological synapses. We describe a single memristive element containing a rich panel of features, which will be of benefit to future neuromorphic hardware systems.