학술논문
PMUT Package Design Optimization via Machine Learning
Document Type
Conference
Author
Source
2024 IEEE 37th International Conference on Micro Electro Mechanical Systems (MEMS) Micro Electro Mechanical Systems (MEMS), 2024 IEEE 37th International Conference on. :971-974 Jan, 2024
Subject
Language
ISSN
2160-1968
Abstract
This work uses supervised learning to optimize the package design with validated experimental results for piezoelectric micromachined ultrasonic transducers (PMUTs) to increase and alter the sound pressure level (SPL). Advancements as compared to the state-of-art include: (1) a neural network model to achieve a mean squared error of less than 0.65 dB 2 post 100 epochs; (2) increased vibration amplitude by 17.9 dBV at the first-mode resonance frequency of 33.5 kHz; and (3) SPL enhancements below the 20 kHz frequency range such as the magnitude increases of more than 60 dBV at 5 kHz. As such, the package design shifts the emitting acoustic energy from the ultrasound to audio range in favor of various applications, including audio speakers.