Putting high-index Cu on the map for high-yield, dry-transferred CVD graphene

Abstract

Reliable, clean transfer and interfacing of 2D material layers is technologically as important as their growth. Bringing both together remains a challenge due to the vast, interconnected parameter space. We introduce a fast-screening descriptor approach to demonstrate holistic data-driven optimization across the entirety of process steps for the graphene-Cu model system. We map the crystallographic dependencies of graphene chemical vapor deposition, interfacial Cu oxidation to decouple graphene, and its dry delamination across inverse pole figures. Their overlay enables us to identify hitherto unexplored (168) higher index Cu orientations as overall optimal. We show the effective preparation of such Cu orientations via epitaxial close-space sublimation and achieve mechanical transfer with very high yield (>95%) and quality of graphene domains, with room-temperature electron mobilities in the range of 40000 cm${}^2$/(Vs). Our approach is readily adaptable to other descriptors and 2D materials systems, and we discuss the opportunities of such holistic optimization.

arXiv comment56 pages, 19 figures

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Cited by in corpus (2)

• An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
• Taxonomy of defects in semi-dry transferred CVD graphene