ABSTRACT
This chapter examines an overview of the potential of deep learning for its wider adoption and state-of-the-art in the application of ALD, while emphasizing the challenges associated with its integration. It presents an overview of the limitless potential deep learning applications in ALD through some case studies. Although, there is a meager amount of research on deep learning applications in thin film research using ALD and other deposition techniques, the list of case studies presented here is not exhaustive. In this research, the authors investigated the use of generative deep learning techniques, namely, variational encoder and generative adversarial neural network to forecast structure zone diagrams in thin film synthesis, particularly in ALD. This research demonstrates a case study of how deep learning techniques were used to characterize ultra-thin epitaxial layers deposited on controlled-shape nano-oxides in three dimensions.
