Low Power IC Design for Health Sensing
| dc.contributor.advisor | Otis, Brian | en_US |
| dc.contributor.author | Gu, Jingren | en_US |
| dc.date.accessioned | 2014-10-13T16:57:48Z | |
| dc.date.available | 2015-12-14T17:55:52Z | |
| dc.date.submitted | 2014 | en_US |
| dc.description | Thesis (Master's)--University of Washington, 2014 | en_US |
| dc.description.abstract | This work proposes a new time-domain integration method to realize Electrochemical Impedance Spectroscopy (EIS). Unlike traditional EIS systems using Fast Fourier Transform (FFT) or Frequency Response Analyzer (FRA) based on a clean quadrature sinusoid stimulus, A single low-frequency, low-amplitude sinusoid stimulus is proposed by using a sinusoid DAC which fully eliminates the analog filter. The response current is integrated by a switched capacitor circuit which is synchronized by the proposed sinusoid DAC. The integration output is sampled and digitized by an 8-bit SAR ADC. The phase and amplitude information of target impedance can be extracted from the time-domain data. The error caused by harmonic generation in the sinusoid DAC can be suppressed by the time-domain integration. The (1×1.1) mm^2 prototype is fabricated in a 130nm CMOS process. It consumes 10µA from a 1.2V supply. Compared with the existing EIS systems, the test results show that the system can obtain a reasonable error with much smaller area and much lower power consumption. | en_US |
| dc.embargo.terms | Restrict to UW for 1 year -- then make Open Access | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.other | Gu_washington_0250O_13516.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/1773/26148 | |
| dc.language.iso | en_US | en_US |
| dc.rights | Copyright is held by the individual authors. | en_US |
| dc.subject | Contact Lens; Electrochemical Impedance Spectroscopy; Low power IC design; Sinusoid DAC | en_US |
| dc.subject.other | Electrical engineering | en_US |
| dc.subject.other | Biomedical engineering | en_US |
| dc.subject.other | electrical engineering | en_US |
| dc.title | Low Power IC Design for Health Sensing | en_US |
| dc.type | Thesis | en_US |
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