NANOWIRE NETWORKS' INTERCONNECTION GRAPHS FROM THEIR PHOTOMICROGRAPHS

Abstract

Self-assemblies of tunable units are being intensively studied as physical systems with signal processing abilities. Specifically, silver nanowire networks (AgNWNs) have demonstrated accumulation, non-linearity, and memory retention with multiple timescales, features that enable a wide variety of neuromorphic implementations. In this study, we aim to extract the interconnection scheme to analyze the experimentally obtained network architecture and, eventually, use it as the input of a previously developed simulation platform. By post-processing photomicrographs of AgNWN, we present a pipeline optimized to extract the interconnection diagram, recognizing the intersections formed among the nanowires, to determine the associated graph for each physical sample. Graph metrics such as degree distribution, community size, clustering coefficient, and path-length are studied to compare the experimental assemblies' attributes to topological models of reference. Small-world, modular, and scale-free are well-known structures in the field of mathematical graphs. The experimental assemblies reveal similarities to both small-world and modular topologies. This communication also studies the impact of artificially removing junctions from the resulting graphs on the previously calculated clustering coefficient and path-length.