AI 中文总结
研究旨在从银纳米线网络显微照片提取互连图用于模拟平台，通过优化管道提取图并分析其指标，发现与小世界和模块化拓扑相似，还研究了去除连接对相关参数的影响。
AI 中文摘要
可调单元的自组装作为具有信号处理能力的物理系统正受到深入研究。银纳米线网络（AgNWNs）具有多种时间尺度的积累、非线性和记忆保持特性。本研究旨在提取互连方案以分析实验获得的网络架构，并将其作为输入用于先前开发的模拟平台。通过对AgNWN显微照片后处理，提出优化管道提取互连图，识别纳米线间交叉点以确定每个物理样本的相关图。通过研究图的度分布等指标，将实验组件属性与参考拓扑模型比较，发现实验组件与小世界和模块化拓扑有相似性。还研究了从图中人工去除连接对聚类系数和路径长度的影响。
英文摘要
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.
