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- W4310585440 abstract "This work reports on a novel metric to assess the severity of in-band spurious modes in high performance microelectromechanical acoustic resonators. Different approaches are discussed to design an informative standard to be implemented as a target function for unsupervised machine learning aided MEMS design algorithms. According to this preliminary analysis, an absolute metric (M), based on the in-band spurious modes admittance excursion, and a regularized version of M (MR) are defined. Both standards are finally tested on the frequency response of sub-GHz X-cut lithium niobate laterally vibrating resonators, succeeding in quantifying the severity of in-band unwanted modes with minimal computational complexity." @default.
- W4310585440 created "2022-12-12" @default.
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- W4310585440 date "2022-10-10" @default.
- W4310585440 modified "2023-10-11" @default.
- W4310585440 title "Spurious Modes Metric Definition for Machine Learning Aided MEMS Design" @default.
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- W4310585440 doi "https://doi.org/10.1109/ius54386.2022.9957468" @default.
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