Wesley Tatum, Diego Torrejon, Patrick O’Neil
Thin films of semiconducting materials will enable stretchable and flexible electronic devices, but these thin films are currently stochastic and inconsistent in their properties and morphologies because processing and chemical conditions influence the mixing and domain size of the different components. By using atomic force microscopy (AFM), a cheap and quick technique, it is possible to spatially resolve and quantify these different domains based on differences in their mechanical properties, which are strongly correlated to their electronic performance. For this project, a library of AFM images has been curated, which includes poly(3- hexylthiophene) that has been processed in different ways (e.g. annealing time and temperature, thin film vs nanowire), as well as thin film mixtures of PTB7-th and PC 71 BM. To analyze these samples, several semantic segmentation methods from the fields of machine learning and topological data analysis are employed. Among these, a Gaussian mixture model utilizing machine learned local geometric features proved effective. From the segmentation, probability distributions describing the mechanical properties of each semantic segment can be obtained, allowing the accurate classification of the various phase domains present in each sample.