微电子机械系统
压电
灵敏度(控制系统)
悬臂梁
话筒
声学
电子工程
材料科学
机械工程
计算机科学
作者
Ahmed Fawzy,Ahmed Magdy,Aya Hossam
标识
DOI:10.1016/j.aej.2021.08.044
摘要
Nowadays, the piezoelectric transduction mechanism has a great concern to be used in the (micro-electromechanical systems) MEMS microphones. In piezoelectric microphones, the thickness, length, width of the piezoelectric and electrode materials are key parameters that need to be optimized in the design loop. The sensitivity is also another vital design factor for MEMS microphones. One common scenario in modeling the sensitivity is to build an electrical equivalent model from lumped components in any simulator. This approach generally requires specialist design expertise and substantial time to build a complete equivalent model. In this paper, a powerful simulation platform to design high-performance cantilever piezoelectric MEMS microphones with sensitivity estimation has been presented. This simulation platform, called MEMS microphone optimizer platform (MMOP), can predict a wide range of key issues related to the successful design of a MEMS Microphone such as the optimum values of piezoelectric material thickness, electrode material thickness, and the length of a cantilever. MMOP offers also the capability to simulate sensitivity directly from the input parameters of the designed model. To validate the proposed simulation platform, a real model of a cantilever MEMS microphone has been studied. In the performed simulations and analysis, sweeping dimensions in micrometer have been considered to predict the best performance. In the proposed model, Aluminum nitride (AlN) and molybdenum (Mo) were utilized as the piezoelectric material and electrode materials, respectively. A high agreement has been found between the theoretical results and the output of the MMOP platform. The platform opens the door for a fast optimized design with accurate results. Finally, MMOP enables a designer to simulate key issues that are specific to cantilever MEMS microphones, including optimized thickness values and predicted sensitivity.
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