Understanding the Relevance of Percolation on Charge Transport in Random ZnO Nanowire Networks

Abstract Understanding conductivity in nanowire networks and nanoelectronics remains challenging due to arbitrary morphologies and conduction hierarchies that are inherent to current modeling approaches. Here, an innovative percolation method utilizing a time‐efficient shortest‐path algorithm is introduced, effectively addressing the arbitrariness in nanowire networks to achieve more realistic conductivity modeling. By applying the percolation framework to arbitrary nanowire assemblies, universalized cluster parameters within the shortest paths are indentified, highlighting the most relevant conductive paths rather than exhaustively examining all nanowire connections. This approach is employed to analyze the two‐step conductivity behavior observed in random ZnO nanowire films. The results indicate that tunneling conduction is the primary mechanism below the percolation threshold, while percolative conductivity becomes dominant beyond this threshold. This model precisely calculates tunneling distances within universalized networks, offering accurate conductivity modeling based on nanowire spacing, which previous models have not fully captured. Furthermore, at low nanowire concentrations, charge transport is confined to a single lowest‐energy barrier path, as evidenced through both numerical and experimental methods. This comprehensive approach integrates theoretical models and experimental applications to enhance the practical use of random nanowire assemblies in real‐world applications. It also enables researchers with limited computational expertise to conduct realistic and accessible conductivity simulations across various nanostructured films and composite materials.