Hybrid Particle Swarm Optimization–Gravitational Search Algorithm Based Detection of Graphene Defects With Electrical Impedance Tomography

Abstract

Recently, graphene has gained a lot of attention in the electronic industry due to its unique properties and to overcome the limits of miniaturization making way for novel devices in the field of electronics. Among the synthesizing methods for growing large graphene films, chemical vapour deposition is one of the promising and common techniques but defects such as cracks, holes, or wrinkles are hard to avoid. The presence of defects influence the electrical properties of graphene thus the local conductivity distribution across the surface of the graphene can characterize its electrical behavior. The difference in conductivity values of the defect and the background can be estimated with electrical impedance tomography. Due to the very high conductivity of graphene, the reconstructed electrical impedance tomography conductivity images suffer from poor spatial resolution. Considering the graphene defect conductivity and the number of defects are known a priori then the unknowns are defect geometry and background conductivity of graphene that are estimated using hybrid particle swarm optimization - gravitational search algorithm. The defect geometries are described by truncated Fourier series coefficient which can represent the complex shapes. Numerical studies are done for graphene characterization with single and multiple defects. Monte Carlo simulations with 20 runs having different noise seed are carried out to evaluate the robustness of the proposed algorithm. Statistical analysis is done and the results of the proposed algorithm are compared against the conventional modified Newton Raphson method and gravitational search algorithm. Experimental studies with graphene sheet 2.5 cm x 2.5 cm with defects are performed for shape estimation. The results showed that the proposed algorithm has a good estimation of background conductivity and defect geometry.