Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/205695
DC Field | Value | |
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dc.title | LOW-NOISE, LOW-POWER, MINIATURIZED SENSOR INTERFACE CIRCUITS FOR WEARABLE HEALTHCARE APPLICATION | |
dc.contributor.author | ZHANG LIAN | |
dc.date.accessioned | 2021-11-08T18:00:26Z | |
dc.date.available | 2021-11-08T18:00:26Z | |
dc.date.issued | 2021-08-02 | |
dc.identifier.citation | ZHANG LIAN (2021-08-02). LOW-NOISE, LOW-POWER, MINIATURIZED SENSOR INTERFACE CIRCUITS FOR WEARABLE HEALTHCARE APPLICATION. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/205695 | |
dc.description.abstract | Bio-potential sensing plays a critical role in extracting real-time ExG signals and facilitating the diagnosis and treatment of diseases such as seizure and cardiovascular abnormalities. The design of low-power and low-noise ExG sensing SoCs in a wearable form factor is essential. The proposed Intrinsic Feedback Capacitor Instrumentation Amplifier (IFCIA) exploits the device capacitance embedded in the MOSFETs as the feedback component of the amplifier. While maintaining a high gain, the small intrinsic capacitor scales down the input capacitor and the total area by 10x with the boosted input impedance of 1.5 GΩ. To solve the exponentially increasing CAPDAC size issue in traditional SAR ADCs, 2-Cycle Analog Front-End (2C-AFE) is designed for EEG recording and epilepsy tracking. The reuse of the amplifier and ADC can effectively improve area-energy efficiency by 8x and 4x. The 2C-AFE realizes 9.8-bit ENOB with only 7-bit CAPDAC, which also relaxes the drivability requirement. | |
dc.language.iso | en | |
dc.subject | ADC, ExG, instrumentation amplifier, low-noise, low-power, miniaturization | |
dc.type | Thesis | |
dc.contributor.department | INTEGRATIVE SCIENCES & ENGINEERING PROG | |
dc.contributor.supervisor | Jerald Yoo | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY (NUSGS) | |
dc.identifier.orcid | 0000-0002-0425-4820 | |
Appears in Collections: | Ph.D Theses (Open) |
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ZhangL.pdf | 4.16 MB | Adobe PDF | OPEN | None | View/Download |
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