Journal
ACS APPLIED ELECTRONIC MATERIALS
Volume -, Issue -, Pages -Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsaelm.2c00628
Keywords
applied machine learning; deep learning; graphene; mass detection; resonator
Funding
- National Research Foundation of Korea (NRF) [RGP00026/2019]
- [NRF-2022R1A2B5B01001640]
- [NRF-2021R1A6A1A10039823]
- [NRF-2018K1A4A3A01064272]
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In this study, we successfully reduced noise interference and improved the accuracy of mass detection in suspended graphene-based nanomechanical resonators using machine learning techniques.
Nanomechanical resonator devices are widely used as ultrasensitive mass detectors for fundamental studies and practical applications. The resonance frequency of the resonators shifts when a mass is loaded, which is used to estimate the mass. However, the shift signal is often blurred by the thermal noise, which interferes with accurate mass detection. Here, we demonstrate the reduction of the noise interference in mass detection in suspended graphene-based nanomechanical resonators, by using applied machine learning. Featurization is divided into image and sequential datasets, and those datasets are trained and classified using 2D and 1D convolutional neural networks (CNNs). The 2D CNN learning-based classification shows a performance with f1-score over 99% when the resonance frequency shift is more than 2.5% of the amplitude of the thermal noise range.
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