Advancing AFM Nanometrology Through Experimental FSI Quantification, Novel Sensor Design, and Fractional ODE-based Material Models

Abstract

Nanometrology, which is the study of materials at the nanoscale, has been an important topic for the material science community at large. Atomic Force Microscopy (AFM) has become an indispensable tool to perform nanometrology. Especially, contact resonance (CR) AFM has been used to perform accurate nanomaterial characterization due to its advantage over other similar modes. While performing CR-AFM, the material is brought into an intimate net-repulsive contact with a cantilever tip to estimate material properties. However, CR-AFM faces challenges while estimating material properties in a few specific cases. This dissertation will mainly ask three different questions related to CR-AFM. The first topic is to predict the fluid forces around a dynamic cantilever to characterize materials in a fluidic environment. Next, a novel AFM cantilever sensor will be designed to tune the stiffness of the sensor according to the sample of interest. This dissertation will also focus on updating the current material models to characterize materials for a wide range of frequencies. Solving the aforementioned issues will improve the CR-AFM method, thus improving the nanoscale material characterization. Historically, CR-AFM has been developed to be operated in a vacuum environment. So, the question becomes what if the cantilever is operated in an environment other than a vacuum? How can a material be characterized inside a fluidic environment? We successfully show that predicting the fluid forces will enable us to estimate the material properties immersed in a liquid environment. Then, the cantilever beam sensor has its bandwidth limitations while probing different sample properties. We have to design a novel sensor that will enable us to probe different samples without replacing the sensor according to the sample. The solution lies in the curvature-induced cantilever plate geometry design as an AFM sensor. The induced curvature in a plate structure enables us to control the inherent stiffness of the plate sensor. This tunability is essential while performing CR-AFM for vastly different sample properties. The third part talks about the material modeling. Classical material models are used to quantify viscoelastic materials. For a few frequency-dependent materials, these classical models do not predict well for a wide range of frequency. We have utilized fractional models to predict material properties for a wide range of frequencies. These fractional models show drastic improvements over the classical integer order models.