Machine Learning Enabled FBAR Digital Twin for Rapid Optimization
| Autor: | Andrew Tweedie, Mihir S Patel, Gergely Hantos, G. Harvey, Gergely Simon |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network business.industry Computer science Resonance 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Resonator Artificial intelligence Thin film business computer 0105 earth and related environmental sciences |
| Zdroj: | 2020 IEEE International Ultrasonics Symposium (IUS). |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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