4.6 Article

Classifiable Limiting Mass Change Detection in a Graphene Resonator Using Applied Machine Learning

期刊

ACS APPLIED ELECTRONIC MATERIALS
卷 -, 期 -, 页码 -

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsaelm.2c00628

关键词

applied machine learning; deep learning; graphene; mass detection; resonator

资金

  1. National Research Foundation of Korea (NRF) [RGP00026/2019]
  2. [NRF-2022R1A2B5B01001640]
  3. [NRF-2021R1A6A1A10039823]
  4. [NRF-2018K1A4A3A01064272]

向作者/读者索取更多资源

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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