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Design and characterization of a MEMS piezoelectric acoustic sensor with the enhanced signal-to-noise ratio

机译:具有增强信噪比的MEMS压电声学传感器的设计与表征

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摘要

The signal-to-noise ratio (SNR) - governed by the sensitivity and noise - determines the usefulness of a MEMS acoustic sensor. Low-SNR poses functional inability in a noisy environment and difficulty in signal processing. The literature is abundant with various approaches to mitigate low-SNR, however, it shows a lack of simultaneous improvement of sensitivity and noise. This paper reports on a MEMS piezoelectric acoustic sensor (PAS) with 7-30 dB higher SNR as compared to the state-of-the-art of PAS. The developed PAS uses analytically modeled aluminum nitride (AlN), and D33 transducer mode with a nominal electrode spacing, i.e., 20 mu m. At 1 kHz frequency, the measured SNR is found to be 67.03 dB which varies from 70 dB to 85 dB in the bandwidth. In addition to the substantial improvement of the SNR, the equivalent input-referred noise (EIN) is found to be 26.97 dB SPL; whereas, the A-weighted noise is 27.23 dBA which is the lowest noise ever analytically and experimentally reported in the state-of-the-art of PAS. Notably, all the measured results are found in full compliance with the analytical predictions. With the analytical and experimental results - in addition to its millimeter size - the developed PAS can be implemented in some cutting edge applications where an acoustic sensor with very high SNR is required like a mobile robot, noise monitoring, position tracking of the incoming sound, and voice-activated human-computer interaction. (C) 2020 Elsevier B.V. All rights reserved.
机译:信噪比(SNR) - 由灵敏度和噪声控制 - 确定MEMS声学传感器的有用性。 Low-SNR在嘈杂的环境中呈现功能,并且在信号处理中难以。文献充分利用各种方法来缓解低SNR,但是,它表明缺乏同时提高敏感性和噪音。本文在与PAS的最新技术相比,MEMS压电声传感器(PAS)报告了7-30dB更高的SNR。开发的PAS使用分析建模的氮化铝(ALN)和D33换能器模式,具有标称电极间距,即20μm。在1 kHz频率下,测量的SNR被发现为67.03 dB,在带宽中的70 dB至85 dB之间变化。除了SNR的大量改进之外,发现等效的输入引用的噪音(EIN)是26.97dB SPL;虽然,A加权噪声是27.23 dBa,其在PAS最先进的噪音和实验上报告的最低噪声。值得注意的是,所有测量结果都以完全符合分析预测来发现。通过分析和实验结果 - 除了毫米尺寸外,还可以在一些切削刃应用中实现开发的PAS,其中需要具有非常高的SNR的声学传感器,如移动机器人,噪声监测,位置跟踪来电,和语音激活的人机交互。 (c)2020 Elsevier B.v.保留所有权利。

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