A contactless single-step process for simultaneous nanoscale patterning and cleaning of large-area graphene
Source abstract
Abstract
Abstract The capability to structure two-dimensional materials (2DMs) at the nanoscale with customizable patterns and over large areas is critical for a number of emerging applications, from nanoelectronics to 2D photonic metasurfaces. However, current technologies, such as photo- and electron-beam lithography, often employing masking layers, can significantly contaminate the materials. Large-area chemical vapour deposition-grown graphene is known to have non-ideal properties already due to surface contamination resulting from the transferring process. Additional contamination through the lithographic process might thus reduce the performance of any device based on the structured graphene. Here, we demonstrate a contactless chemical-free approach for simultaneous patterning and cleaning of self-supporting graphene membranes in a single step. Using energetic ions passing through a suspended mask with pre-defined nanopatterns, we deterministically structure graphene with demonstrated feature size of 15 nm, approaching the performance of small-area focused ion beam techniques and extreme ultraviolet lithography. Our approach, however, requires only a broad beam, no nanoscale beam positioning and enables large area patterning of 2DMs. Simultaneously, in regions surrounding the exposed areas, contaminations commonly observed on as-grown graphene targets, are effectively removed. This cleaning mechanism is attributed to coupling of surface diffusion and sputtering effects of adsorbed surface contaminants. For applications using 2DMs, this simultaneous patterning and cleaning mechanism may become essential for preparing the nanostructured materials with improved cleanliness and hence, quality.
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Research context
OpenAlex topics: Graphene research and applications · Nanopore and Nanochannel Transport Studies · Energy Harvesting in Wireless Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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