Condensed Matter > Mesoscale and Nanoscale Physics
[Submitted on 24 Apr 2024]
Title:Neural network-based recognition of multiple nanobubbles in graphene
View PDF HTML (experimental)Abstract:We present a machine learning method for swiftly identifying nanobubbles in graphene, crucial for understanding electronic transport in graphene-based devices. Nanobubbles cause local strain, impacting graphene's transport properties. Traditional techniques like optical imaging are slow and limited for characterizing multiple nanobubbles. Our approach uses neural networks to analyze graphene's density of states, enabling rapid detection and characterization of nanobubbles from electronic transport data. This method swiftly enumerates nanobubbles and surpasses conventional imaging methods in efficiency and speed. It enhances quality assessment and optimization of graphene nanodevices, marking a significant advance in condensed matter physics and materials science. Our technique offers an efficient solution for probing the interplay between nanoscale features and electronic properties in two-dimensional materials.
Submission history
From: Nojoon Myoung Prof. [view email][v1] Wed, 24 Apr 2024 05:24:55 UTC (1,308 KB)
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