Artificial intelligence in atomic layer deposition

Atomic layer deposition (ALD) is a vapor-phase thin-film deposition technique offering precise atomic-scale control over film thickness and conformality. Widely used across diverse fields including microelectronics, energy storage, and biomaterials, traditional optimization of ALD processes typically relies on trial-and-error experimentation or computational simulations. Recently, artificial intelligence (AI) and machine learning (ML) methods have emerged as powerful tools to significantly accelerate and improve ALD process optimization, material discovery, and fundamental understanding of ALD chemistry. In this review, we highlight recent advancements in the intersection of ALD and AI/ML, examining their roles in process optimization, new material development, reaction mechanism exploration, and the application of advanced algorithms, including large language models. Additionally, we introduce a bibliometric approach to systematically collect and analyze relevant scientific literature. Finally, we outline existing challenges and provide insights into the future potential of integrating AI with ALD to drive further innovation in materials science and engineering.