A 79.2dB-SNDR Slope-Adaptive Dynamic Zoom-and-Track Incremental ΔΣ Neural Recording Frontend with Resolution-Preservative 192mV/ms Transient Tracking

Sungjin Oh, Hyunsoo Song, Jose R.L. Ruiz, Wangbo Chen, Sung Yun Park, Michael P. Flynn, Euisik Yoon

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

Abstract

We present an incremental ΔΣ frontend that can resolve μ V-level neural signals superimposed with large (> 100m V) and fast (> 100mV/ms) interferences without quality degradation. Unlike conventional approaches that stretch conversion range to accept the large input by sacrificing resolution, the proposed work tracks the large transient with constant high-resolution but variable tracking speed adaptive to its input slope. Thus, this frontend acquires neural information without quality loss even when exposed to large and fast artifacts that overwhelm small neural signals. With the proposed high-resolution tracking scheme, this work achieves the highest SNDR (79.2dB) and FoMSNDR (165.7dB) among the state-of-the-art frontends using the conventional transient tracking techniques.

Original languageEnglish
Title of host publication2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350361469
DOIs
Publication statusPublished - 2024
Event2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024 - Honolulu, United States
Duration: 2024 Jun 162024 Jun 20

Publication series

NameDigest of Technical Papers - Symposium on VLSI Technology
ISSN (Print)0743-1562

Conference

Conference2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024
Country/TerritoryUnited States
CityHonolulu
Period24/6/1624/6/20

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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