@inproceedings{5af284bb484c46f5b1a3ccfc42b77ce6,
title = "Machine Learning Enabled FBAR Digital Twin for Rapid Optimization",
abstract = "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.",
keywords = "FBAR, Machine learning, Neural network, Regression model",
author = "Gergely Simon and Hantos, {Gergely B.} and Patel, {Mihir S.} and Andrew Tweedie and Gerald Harvey",
year = "2020",
month = nov,
day = "17",
doi = "10.1109/IUS46767.2020.9251797",
language = "English",
series = "IEEE International Ultrasonics Symposium",
publisher = "IEEE",
booktitle = "2020 IEEE International Ultrasonics Symposium (IUS)",
address = "United States",
note = "2020 IEEE International Ultrasonics Symposium, IUS 2020 ; Conference date: 07-09-2020 Through 11-09-2020",
}