Characterization of thin films by neural networks and analytical approximations

In this work we focus on the characterization of thin films by Electrostatic Force Microscopy (EFM). We use the estimations made by Artificial Neural Networks (ANNs) trained by numerical results from the Generalized Image Charge Method (GICM). The ANN outputs suggest that an effective dielectric constant can be defined for any thin film sample. The definition of an effective dielectric constant allows us to include complex thin film samples in analytical approximations previously developed for much simpler surfaces. From the new analytical formulation we can conclude that the dielectric thin films interact in a complex way. Moreover, the metallic plate below the surface changes the effective dielectric constant, suggesting that its contribution cannot be isolated from the one of the dielectric plates.