Effect of junction-to-nanowire resistance ratio on the percolation conductivity and critical exponents of nanowire networks

Using Monte Carlo simulations, we study the effect of the junction-to-nanowire resistance ratio on the percolation transport in nanowire networks. By varying the resistance ratio over a span of six orders of magnitude, we first investigate its effect on the conductivity of nanowire networks at different values of five parameters, namely, nanowire density, nanowire length, device width, nanowire alignment, and curviness. We find that the network conductivity decreases with an increase in the resistance ratio, which is most pronounced close to the percolation threshold. We also find that the network resistivity depends linearly on the resistance ratio in the junction-dominated regime, with the slope increasing as the network approaches the percolation threshold. For nanowire alignment, however, the minimum slope occurs for a partially aligned network, rather than a completely random one. Next, we study the effect of the resistance ratio on the percolation critical exponents for each of the five parameters. We find that that the critical exponents increase as the resistance ratio increases from a nanowire-dominated to a junction-dominated network; however, the amount of this increase depends on the parameter being varied. We explain these findings by physical arguments based on percolation transport. These results, which can be applied to any two-dimensional network comprised of one-dimensional nanoelements, show that Monte Carlo simulations are crucial for not only studying the physics of percolation transport in nanowire networks, but also enabling predictive modeling and optimization of nanowire networks for a wide range of device applications, such as transparent conductors and resistive switching memory.