A MOSFET-only Delta-Sigma modulator for implantable neural signal sensors

Rui Fan, Jian Xu, Xiaobo Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A low voltage high linearity MOSFET-only Delta-Sigma modulator for implantable neural signal sensors is presented in this paper. In order to reduce the capacitor area significantly, most of the capacitors in the modulator are implemented by the single PMOSFET MOSCAPs in inversion region. The rest capacitors are realized by series compensated MOSCAPs to ensure high linearity over the wide swing. A DC level shift technique is employed in the first integrator to guarantee the PMOSFET in the inversion region and thus extend the input signal swing. Besides, a 4-bit quantizer is applied to increase the signal-to-noise-and-distortion-rate (SNDR) and decrease the output swing of each integrator for the linearity optimization of MOSCAPs. The bottom terminal parasitic effect of PIP (poly-insulator-poly) capacitors is also eliminated completely as a result of MOSCAPs application. The modulator is simulated in a 0.35μm n-well standard CMOS process and achieves 104dB SNDR at 8k-Hz signal bandwidth. Compared with the PIP capacitor design, this work reduces about 77% of the total capacitor area.

Original languageEnglish (US)
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
Pages1282-1285
Number of pages4
StatePublished - Dec 1 2010
EventInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010 - Kowloon, Hong Kong
Duration: Mar 17 2010Mar 19 2010

Publication series

NameProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

Other

OtherInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
Country/TerritoryHong Kong
CityKowloon
Period3/17/103/19/10

Keywords

  • Delta-Sigma modulator
  • Inversion region
  • MOSCAP
  • MOSFET-only

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