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

Miri Seo, Eunseo Yang, Dong Hoon Shin, Yugyeong Je, Chirlmin Joo, Kookjin Lee, Sang Wook Lee

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)5184-5190
Number of pages7
JournalACS Applied Electronic Materials
Issue number11
StatePublished - 22 Nov 2022

Bibliographical note

Funding Information:
This research was supported by the Basic Research Program (NRF-2022R1A2B5B01001640 and NRF-2021R1A6A1A10039823) and the Global Research and Development Center Program (NRF-2018K1A4A3A01064272) through the National Research Foundation of Korea (NRF) and also supported by the Human Frontier Science Program (RGP00026/2019).

Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.


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


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