Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films

Abstract

This study focuses on characterizing grain size and distribution in vanadium dioxide (VO2) thin films using Atomic Force Microscope (AFM) images. Traditional segmentation methods face challenges in accurately detecting grains, and this paper addresses these limitations by employing machine learning-based classification approaches, defining three classes: grains, joints or dividing lines, and the background. Various classification methods, including supervised, unsupervised, and thresholding-based approaches, were investigated, demonstrating the superiority of machine learning-based methods over traditional segmentation techniques. Deep Learning (DL) models, such as Convolutional Neural Networks (CNN), Deep Belief Networks (DBN), and Deep Boltzmann Machines (DBM), achieved the best results in accuracy and precision. The DBN algorithm, in particular, outperformed other methods with a 93.66% accuracy in grain detection. This could be attributed to its probabilistic modeling and effective handling of challenging pixels, making it a powerful tool for accurate grain size and distribution analysis.