Machine learning-based exploration of molecular design descriptors for area-selective atomic layer deposition (AS-ALD) precursors.

Context: Area-selective atomic layer deposition (AS-ALD) is a thin film deposition technique developed using conventional ALD by considering the surface chemical nature of the substrate. Selecting appropriate precursors is a critical step in developing an efficient AS-ALD process with high deposition selectivity. However, the current efficiency of research on viable AS-ALD precursors is limited because of the absence of theoretical design rules for precursor chemical structures.

Low-temperature growth of MoS on polymer and thin glass substrates for flexible electronics.

Recent advances in two-dimensional semiconductors, particularly molybdenum disulfide (MoS), have enabled the fabrication of flexible electronic devices with outstanding mechanical flexibility. Previous approaches typically involved the synthesis of MoS on a rigid substrate at a high temperature followed by the transfer to a flexible substrate onto which the device is fabricated. A recurring drawback with this methodology is the fact that flexible substrates have a lower melting temperature than the MoS growth process, and that the transfer process degrades the electronic properties of MoS.

Thermal Annealing of Molecular Layer-Deposited Indicone Toward Area-Selective Atomic Layer Deposition.

Area-selective atomic layer deposition (AS-ALD) is a promising technique for fine nanoscale patterning, which may overcome the drawbacks of conventional top-down approaches for the fabrication of future electronic devices. However, conventional materials and processes often employed for AS-ALD are inadequate for conformal and rapid processing. We introduce a new strategy for AS-ALD based on molecular layer deposition (MLD) that is compatible with large-scale manufacturing.