Decoding the metallic bridging dynamics in nanogap atomic switches

Abstract

Atomic switches are promising candidates as the basic building blocks for large-scale neuromorphic networks due to their tunable switching behaviors. Several neuromorphic components based on atomic switches have been demonstrated, including artificial synapses, artificial neurons and short-term to long-term memory, making it possible to construct neuromorphic systems using a unified device. Although the mechanism of atomic switches has been actively studied, most of the discussions in previous studies are qualitative and failed to provide a comprehensive view of the dynamics that can precisely describe the metallic bridging under an electric field. In this paper, we designed a gap-type atomic switch and realized various switching behaviors, including both volatile and non-volatile resistive switching. Employing advanced microanalysis technology, we experimentally studied the switching mechanism and captured the nanoscale metallic filament in gap-type atomic switches. Furthermore, based on the experimental findings as well as on the electrochemistry fundamental and electron tunneling effect, we proposed a physical model that precisely reproduced the sophisticated switching behaviors. Our model mathematically described the growth/shrinkage dynamics of nanoscale metallic filaments, providing a direction for studying the switching behaviors from a quantitative view. The simulation results are in good agreement with the experimental findings in both DC sweep and pulse operation modes. In addition, we have demonstrated neuronal tonic spiking and short-term to long-term memory in experiment and simulation, indicating that our model can be applied to the circuit level simulation of large-scale atomic switch arrays for neuromorphic applications.