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Zero-Power Sensing and Processing With Piezoelectric Resonators.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Zero-Power Sensing and Processing With Piezoelectric Resonators./
作者:	Shkel, Anton A.
面頁冊數:	1 online resource (197 pages)
附註:	Source: Dissertations Abstracts International, Volume: 81-12, Section: B.
Contained By:	Dissertations Abstracts International81-12B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27807967click for full text (PQDT)
ISBN:	9781392662120

Zero-Power Sensing and Processing With Piezoelectric Resonators.
Shkel, Anton A.

Zero-Power Sensing and Processing With Piezoelectric Resonators. - 1 online resource (197 pages)

Source: Dissertations Abstracts International, Volume: 81-12, Section: B.

Thesis (Ph.D.)--University of Southern California, 2018.

Includes bibliographical references

This dissertation presents several micro-electromechanical (MEMS) sensors and devices based on thin-film piezoelectric materials to enable zero-power and ultra-low-power intelligent systems in power-constrained scenarios.A MEMS resonant microphone array has been developed and evaluated as a mechanical filter-bank front end for speech recognition and respiratory monitoring experiments. These experiments consistently demonstrate robustness to ambient noise relative to traditional digital signal processing methods. With cepstral features computed from 40 ms frames, we measured up to a 57.8% increase in F1 score by using a resonant-array processing method for a signal-to-noise ratio of -26 dB. Using spectro-temporal cepstral features classified with a dense neural network, improvements of up to 58.8% were measured for signal-to-noise ratios of -26 dB over an equivalent digital filter implementation.A complete sensing and low-power signal processing system was developed and evaluated to integrate array-based respiratory sound sensing, vibration energy harvesting, and wireless transmission of data upon detection of wheezing. Using an ultra-low power processor with 16 kB SRAM and 24 MHz processing speed, a resonant-array based respiratory classification system was implemented with classification cycles completing in a 0.46 second period with an average power consumption of 0.596 mW, a factor of 11.1 improvement over a typical implementation.A method for passively amplifying the sensitivity of the developed microphone arrays was hypothesized, modeled, fabricated, and experimentally validated. The method, based on a micro-fabricated Helmholtz resonator cavity, was shown to improve peak sensitivity and quality factor of resonant microphones by up to 13.9 in centimeter-scale devices, and by up to 2.16 in micro-scale devices.A zero-power wireless authentication system based on FBARs was fabricated, simulated, and experimentally evaluated as a unique method for wireless and passive detection of tampering activity within integrated circuits. This proof-of-concept system has a RFID interrogation frequency of 2.6 GHz, and an energy harvester generating a 5 V pulse was demonstrated to permanently alter the RFID spectral characteristics. Piezoelectric energy harvesters were developed on both bulk ceramic and flexible substrates, and were characterized for harvesting energy from mechanical vibrations.These demonstrations of low-power systems based on MEMS resonators and thin-film piezoelectrics provide several creative solutions to emerging power-constrained applications, including wearable health monitoring, distributed sensor nodes, and internet-of-things.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9781392662120Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

PiezoelectricsIndex Terms--Genre/Form:

542853
Electronic books.

Zero-Power Sensing and Processing With Piezoelectric Resonators.
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520 $a This dissertation presents several micro-electromechanical (MEMS) sensors and devices based on thin-film piezoelectric materials to enable zero-power and ultra-low-power intelligent systems in power-constrained scenarios.A MEMS resonant microphone array has been developed and evaluated as a mechanical filter-bank front end for speech recognition and respiratory monitoring experiments. These experiments consistently demonstrate robustness to ambient noise relative to traditional digital signal processing methods. With cepstral features computed from 40 ms frames, we measured up to a 57.8% increase in F1 score by using a resonant-array processing method for a signal-to-noise ratio of -26 dB. Using spectro-temporal cepstral features classified with a dense neural network, improvements of up to 58.8% were measured for signal-to-noise ratios of -26 dB over an equivalent digital filter implementation.A complete sensing and low-power signal processing system was developed and evaluated to integrate array-based respiratory sound sensing, vibration energy harvesting, and wireless transmission of data upon detection of wheezing. Using an ultra-low power processor with 16 kB SRAM and 24 MHz processing speed, a resonant-array based respiratory classification system was implemented with classification cycles completing in a 0.46 second period with an average power consumption of 0.596 mW, a factor of 11.1 improvement over a typical implementation.A method for passively amplifying the sensitivity of the developed microphone arrays was hypothesized, modeled, fabricated, and experimentally validated. The method, based on a micro-fabricated Helmholtz resonator cavity, was shown to improve peak sensitivity and quality factor of resonant microphones by up to 13.9 in centimeter-scale devices, and by up to 2.16 in micro-scale devices.A zero-power wireless authentication system based on FBARs was fabricated, simulated, and experimentally evaluated as a unique method for wireless and passive detection of tampering activity within integrated circuits. This proof-of-concept system has a RFID interrogation frequency of 2.6 GHz, and an energy harvester generating a 5 V pulse was demonstrated to permanently alter the RFID spectral characteristics. Piezoelectric energy harvesters were developed on both bulk ceramic and flexible substrates, and were characterized for harvesting energy from mechanical vibrations.These demonstrations of low-power systems based on MEMS resonators and thin-film piezoelectrics provide several creative solutions to emerging power-constrained applications, including wearable health monitoring, distributed sensor nodes, and internet-of-things.
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