A computational study of the impact of voids on the percolation electronic conductivity of nanowire networks

Understanding the impact of voids, which could be present due to the deposition process or introduced intentionally, on the electronic properties of nanowire networks is critical for applications such as transparent conductive electrodes, thin film transistors, sensing, and hardware security. Employing Monte Carlo simulations, we first compute the percolation probability in these networks as a function of nanowire density for different void sizes. Assuming a Gaussian percolation probability density function, we find that both the mean and standard deviation increase with increasing void size. We then compute the relative conductivity change as a function of nanowire density for different void sizes and find that it increases as the square root of the void area. We also study the effect of the location and aspect ratio of the void, the finite size of the network, and the junction to nanowire resistance ratio on the percolation probability and relative conductivity change of the nanowire network. Furthermore, we generate curvy nanowires using third order Bezier curves characterized by the curviness angle and aligned nanowires using an orientation characterized by the alignment angle. We then investigate the impact of voids on networks consisting of curvy and aligned nanowires. These computational results demonstrate the significant impact voids have on the electronic properties of two-dimensional networks consisting of one-dimensional nanowires.