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A Modeling and Feasibility Study of a Micro-Machined Microphone Based on a Field-Effect Transistor and an Electret for a Low-Frequency Microphone

Miniaturized capacitive microphones often show sensitivity degradation in the low-frequency region due to electrical and acoustical time constants. For low-frequency sound detection, conventional systems use a microphone with a large diaphragm and a large back chamber to increase the time constant....

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-09, Vol.20 (19), p.5554
Main Authors: Shin, Kumjae, Kim, Chayeong, Sung, Min, Kim, Junsoo, Moon, Wonkyu
Format: Article
Language:English
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Summary:Miniaturized capacitive microphones often show sensitivity degradation in the low-frequency region due to electrical and acoustical time constants. For low-frequency sound detection, conventional systems use a microphone with a large diaphragm and a large back chamber to increase the time constant. In order to overcome this limitation, an electret gate on a field-effect transistor (ElGoFET) structure was proposed, which is the field-effect transistor (FET) mounted diaphragm faced on electret. The use of the sensing mechanism consisting of the integrated FET and electret enables the direct detection of diaphragm displacement, which leads its acoustic senor application (ElGoFET microphone) and has a strong ability to detect low-frequency sound. We studied a theoretical model and design for low-frequency operation of the ElGoFET microphone prototype. Experimental investigations pertaining to the design, fabrication, and acoustic measurement of the microphone were performed and the results were compared to our analytical predictions. The feasibility of the microphone as a low-frequency micro-electromechanical system (MEMS) microphone, without the need for a direct current bias voltage (which is of particular interest for applications requiring miniaturized components), was demonstrated by the flat-band frequency response in the low-frequency region.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20195554