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Title:

Machine Learning Strategies for Freeform PMUTs Design

Document type:
Konferenzbeitrag
Contribution type:
Poster
Author(s):
Xu, J.; Schrag, G.; Shao, Z.
Pages contribution:
4
Abstract:
This study investigates the efficacy of multiple machine learning (ML) strategies for optimizing the design of freeform Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) by leveraging a data-centric methodology. We devise a comprehensive four-stage optimization framework comprising a freeform PMUT shape generator, a feature extractor, a finite element analyzer, and ML estimators. The ML evaluation compared to the finite element analysis reveals that the leading ML estimator accomplished...     »
Keywords:
Freeform PMUTs, Machine learning, Data-driven Analysis, Sensitivity, Fractional Bandwidth; Xu, J. ; Schrag, G. ; Shao, Z.; PMUT; ML; Data; methodology; frequency
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Book / Congress title:
IEEE International Ultrasonics Symposium (IUS)
Volume:
TMS: Transducer and System Modeling and Characterization (Poster)
Date of congress:
22.-26.09.2024
Year:
2024
Pages:
4
TUM Institution:
CIT, EE, Professorship of Microsensors and Actuators
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