A computational framework for quantifying electrical conductance in metallic nanomesh using image processing and computer vision technologies

Abstract

This study introduces a computational framework for precisely quantifying electrical conductance in metallic nanomesh structures, leveraging advanced image processing and computer vision algorithms. Nanomesh images obtained via scanning electron microscopy are subjected to preprocessing operations, including threshold-based binary conversion and convolution techniques, to mitigate defects and delineate conductive pathways. A rigorous equivalent electrical path model is subsequently established through keypoint identification via mean-shift segmentation. Kirchhoff's current law is applied to the model to deduce the conductance of the nanomesh. The computationally estimated conductance results are stringently validated against experimental measurements, confirming the model's accuracy. Further, the framework is applied to ascertain the resistivity of nanoscale Ag films deposited on glass substrates, revealing a resistivity higher than that of bulk materials—a finding corroborated by both computational predictions and experimental data. The methodology can be a robust, automated analytical tool for assessing conductance in comparable nanostructured materials.