Characterizing Defects Inside Hexagonal Boron Nitride Using Random Telegraph Signals in van der Waals 2D Transistors
Year: 2024
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide Single-crystal hexagonal boron nitride (hBN) is used extensively in many two-dimensional electronic and quantum devices, where defects significantly impact performance. Therefore, characterizing and engineering hBN defects are crucial for advancing these technologies. Here, we examine the capture and emission dynamics of defects in hBN by utilizing low-frequency noise (LFN) spectroscopy in hBN-encapsulated and graphene-contacted MoS 2 field-effect transistors (FETs). The low disorder of this heterostructure allows the detection of random telegraph signals (RTS) in large device dimensions of 100 μm 2 at cryogenic temperatures. Analysis of gate bias- and temperature-dependent LFN data indicates that RTS originates from a single trap species within hBN. By performing multispace density functional theory (MS-DFT) calculations on a gated defective hBN/MoS 2 heterostructure model, we assign substitutional carbon atoms in boron sites as the atomistic origin of RTS. This study demonstrates the utility of LFN spectroscopy combined with MS-DFT analysis on a low-disorder all-vdW FET as a powerful means for characterizing the atomistic defects in single-crystal hBN.
Source: ACS Nano
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funders: National Science Foundation, U.S. Department of Energy, City University of New York, Advanced Storage Research Consortium, National Research Foundation, Ministry of Education, Culture, Sports, Science and Technology, Korea Institute of Science and Technology Information, National Research Foundation of Korea, Korea Advanced Institute of Science and Technology, Office of Science, Japan Society for the Promotion of Science, National Supercomputing Center, Korea Institute of Science and Technology Information, Division of Civil, Mechanical and Manufacturing Innovation, Brookhaven National Laboratory
OpenAlex ID: W4402944160
