Computational Ab Initio Approaches for Area-Selective Atomic Layer Deposition: Methods, Status, and Perspectives

Abstract

Area-selective atomic layer deposition (AS-ALD) has emerged as a transformative technique in nanotechnology, enabling the precise deposition of materials on designated substrates while preventing unwanted growth on adjacent surfaces. This capability is critical for applications in microelectronics, catalysis, and energy technologies. Computational methods, particularly density functional theory (DFT), are indispensable for uncovering the mechanisms underlying AS-ALD, providing insights into surface interactions, selectivity mechanisms, and precursor design. This review introduces the theoretical background of computational techniques applied to AS-ALD and provides a detailed overview of their applications. Special emphasis is placed on the use of ab initio methods to explore surface chemistry, optimize precursor and inhibitor properties, and improve selectivity. A comprehensive overview of the literature is given with an analysis of research questions targeted, and methods used. By consolidating the state of knowledge and identifying future challenges, this work aims to guide researchers in further leveraging computational approaches to drive innovations in AS-ALD processes.