Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing

Memristors, or memristive devices, have attracted tremendous interest in neuromorphic hardware implementation. However, the high electric-field dependence in conventional filamentary memristors results in either digital-like conductance updates or gradual switching only in a limited dynamic range. Here, we address the switching parameter, the reduction probability of Ag cations in the switching medium, and ultimately demonstrate a cluster-type analogue memristor. Ti nanoclusters are embedded into densified amorphous Si for the following reasons: low standard reduction potential, thermodynamic miscibility with Si, and alloy formation with Ag. These Ti clusters effectively induce the electrochemical reduction activity of Ag cations and allow linear potentiation/depression in tandem with a large conductance range (similar to 244) and long data retention (similar to 99% at 1 hour). Moreover, according to the reduction potentials of incorporated metals (Pt, Ta, W, and Ti), the extent of linearity improvement is selectively tuneable. Image processing simulation proves that the Ti-4.8%:a-Si device can fully function with high accuracy as an ideal synaptic model.

Automated summary

In this paper, a new cluster-type analogue memristor is demonstrated by incorporating Ti nanoclusters into densified amorphous Si, inducing electrochemical reduction activity of Ag cations for linear potentiation/depression with a large conductance range and long data retention. The linearity improvement is selectively tuneable by adjusting the reduction potentials of incorporated metals, and the Ti-4.8%:a-Si device functions as an ideal synaptic model with high accuracy in image processing simulation.