Reducing the metal-graphene contact resistance through laser-induced defects

Authors: Vikas Jangra, Satender Kataria, Max C. Lemme

arXiv: 2402.07151v1 - DOI (physics.app-ph)
30 pages

Abstract: Graphene has been extensively studied for a variety of electronic and optoelectronic applications. The reported contact resistance between metal and graphene, or rather its specific contact resistance (R{_C}), ranges from a few tens of {\Omega} {\mu}m up to a few k{\Omega} {\mu}m. Manufacturable solutions for defining ohmic contacts to graphene remain a subject of research. Here, we report a scalable method based on laser irradiation of graphene to reduce the R{_C} in nickel-contacted devices. A laser with a wavelength of {\lambda} = 532 nm is used to induce defects at the contact regions, which are monitored \textit{in-situ} using micro-Raman spectroscopy. Physical damage is observed using \textit{ex-situ} atomic force and scanning electron microscopy. The transfer line method (TLM) is used to extract R{_C} from back-gated graphene devices with and without laser treatment under ambient and vacuum conditions. A significant reduction in R{_C} is observed in devices where the contacts are laser irradiated, which scales with the laser power. The lowest R{_C} of about 250 {\Omega} {\mu}m is obtained for the devices irradiated with a laser power of 20 mW, compared to 900 {\Omega} {\mu}m for the untreated devices. The reduction is attributed to an increase in defect density, which leads to the formation of crystallite edges and in-plane dangling bonds that enhance the injection of charge carriers from the metal into the graphene. Our work suggests laser irradiation as a scalable technology for R{_C} reduction in graphene and potentially other two-dimensional materials.

Submitted to arXiv on 11 Feb. 2024

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.