A 0.0094mm 2 /Channel Time-Based Beat Frequency ADC in 65nm CMOS for Intra-Electrode Neural Recording

Luke Everson, Somnath Kundu, Gang Chen, Zhi Yang, Timothy J. Ebner, Chris H. Kim

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

1 Scopus citations

Abstract

A digital-intensive, low-area, time-based ADC optimized for in-situ neural recording is fabricated in a 65nm test chip and validated with in-vivo data. The intrinsic inversely proportional gain of a beat frequency based quantizer allows recording of sub-millivolt neural signals without any sophisticated amplifiers or filters. A low-area analog-front-end (AFE) is implemented with a standard digital logic inverter transimpedance amplifier and tunable low pass and high pass filters. The test chip achieves 20.9dB SNDR for a 1mVpp input at 416Hz with a bandwidth of 4.2 kHz and consumes 52μW at 0.8V. In-vivo evoked potentials and spontaneous activity were measured directly from a mouse cerebellum without any external components, validating the efficacy of the aggressive tradeoffs. These results are achieved in an area of 0.0094mm 2 /channel, including on-chip AC coupling and filter passives, which makes this an attractive architecture for complete integration in ultra-high channel count neural recording systems.

Original languageEnglish (US)
Title of host publication2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538636039
DOIs
StatePublished - Dec 20 2018
Event2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Cleveland, United States
Duration: Oct 17 2018Oct 19 2018

Publication series

Name2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings

Other

Other2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Country/TerritoryUnited States
CityCleveland
Period10/17/1810/19/18

Bibliographical note

Funding Information:
This research was supported in part by NSF IGERT grant DGE-1069104 and NIH grant NS 18338.

Funding Information:
ACKNOWLEDGEMENTS: This research was supported in part by NSF IGERT grant DGE-1069104 and NIH grant NS 18338.

Publisher Copyright:
© 2018 IEEE.

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