Nanowire networks' interconnection graphs from their photomicrographs

Abstract

Self-assemblies of tunable units are being intensively studied as physical systems with signal-processing capabilities. 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 input to 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. A graph is a collection of nodes and edges whose properties can be associated with different electrical responses. It is thus possible to study graphs' metrics, such as the degree distribution, community size, clustering coefficient, and path-length, to compare the experimental assemblies' attributes to those of topological models of reference. Small-world, modular, and scale-free are well-known structures in the field of mathematical graphs. By analyzing the degree distribution, the adjacency matrices, among other useful representation means, the experimental assemblies reveal similarities to both small-world and modular topologies. All the mentioned analyses were conducted considering the interconnection scheme obtained from zenithal-view optical images, which overestimates the number of NWs' interconnections (due to the impossibility of distinguishing real junctions from spurious cross-points between vertically displaced NWs). For that reason, this communication also studies the impact of artificially removing junctions from the resulting graphs on the previously calculated clustering coefficient and path length.