Microelectromechanical system-based wireless microsystem for brain interfacing

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Publication Type dissertation
School or College College of Engineering
Department Electrical & Computer Engineering
Author Chen, Lingyao
Title Microelectromechanical system-based wireless microsystem for brain interfacing
Date 2013-12
Description This thesis presents the design, fabrication and characterization of a microelectromechanical system (MEMS) based complete wireless microsystem for brain interfacing, with very high quality factor and low power consumption. Components of the neuron sensing system include TiW fixed-fixed bridge resonator, MEMS oscillator based action-potential-to-RF module, and high-efficiency RF coil link for power and data transmissions. First, TiW fixed-fixed bridge resonator on glass substrate was fabricated and characterized, with resonance frequency of 100 - 500 kHz, and a quality factor up to 2,000 inside 10 mT vacuum. The effect of surface conditions on resonator's quality factor was studied with 10s of nm Al2O3 layer deposition with ALD (atomic layer deposition). It was found that MEMS resonator's quality factor decreased with increasing surface roughness. Second, action-potential-to-RF module was realized with MEMS oscillator based on TiW bridge resonator. Oscillation signal with frequency of 442 kHz and phase noise of -84.75 dBc/Hz at 1 kHz offset was obtained. DC biasing of the MEMS oscillator was modulated with neural signal so that the output RF waveform carries the neural signal information. Third, high-efficiency RF coil link for power and data communications was designed and realized. Based on the coupled mode theory (CMT), intermediate resonance coil was introduced and increased voltage transfer efficiency by up to 5 times. Finally, a complete neural interfacing system was demonstrated with board-level integration. The system consists of both internal and external systems, with wireless powering, wireless data transfer, artificial neuron signal generation, neural signal modulation and demodulation, and computer interface displaying restored neuron signal.
Type Text
Publisher University of Utah
Dissertation Institution University of Utah
Dissertation Name Doctor of Philosophy
Language eng
Rights Management Copyright © Lingyao Chen 2013
Format Medium application/pdf
Format Extent 2,248,762 bytes
Identifier etd3/id/2644
ARK ark:/87278/s6574m64
Setname ir_etd
ID 196219
Reference URL https://collections.lib.utah.edu/ark:/87278/s6574m64
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