Machine Learning Enabled FBAR Digital Twin for Rapid Optimization

Gergely Simon, Gergely B. Hantos, Mihir S. Patel, Andrew Tweedie, Gerald Harvey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


In this paper we discuss a machine learning-based method to obtain a digital twin of a Thin Film Bulk Acoustic Wave Resonator (TFBAR) that can be used as a surrogate for simulations to estimate resonance frequencies of devices. Normalized root mean square error values better than 0.04% and 0.1% were achieved for 1D and 2D models, respectively. Training times for neural networks were 20 s for 2000 epochs and hundreds of datasets.

Original languageEnglish
Title of host publication2020 IEEE International Ultrasonics Symposium (IUS)
ISBN (Electronic)9781728154480
Publication statusPublished - 17 Nov 2020
Event2020 IEEE International Ultrasonics Symposium - Las Vegas, United States
Duration: 7 Sept 202011 Sept 2020

Publication series

NameIEEE International Ultrasonics Symposium
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727


Conference2020 IEEE International Ultrasonics Symposium
Abbreviated titleIUS 2020
Country/TerritoryUnited States
CityLas Vegas


  • FBAR
  • Machine learning
  • Neural network
  • Regression model

ASJC Scopus subject areas

  • Acoustics and Ultrasonics


Dive into the research topics of 'Machine Learning Enabled FBAR Digital Twin for Rapid Optimization'. Together they form a unique fingerprint.

Cite this