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A 1024-Channel 268 nW/pixel 36x36 μm2/ch Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces
MoonHyung Jang2; Wei-Han Yu1; Changuk Lee3; Maddy Hays2; Pingyu Wang2; Nick Vitale2; Pulkit Tandon2; Pumiao Yan2; Pui-In Mak1; Youngcheol Chae3; E.J. Chichilnisky2; Boris Murmann2; Dante G. Muratore4
2023-07-24
Conference Name2023 Symposium on VLSI Technology and Circuits
Source PublicationIEEE Symposium on VLSI Technology and Circuits
Volume2023-June
Conference Date11-16, June, 2023
Conference PlaceKyoto, Japan
CountryJapan
Publication PlaceIEEE Xplore
PublisherIEEE
Abstract

This paper presents a neural recording IC featuring lossy compression during digitization, thus preventing data deluge and enabling a compact active digital pixel design. The wired-OR-based compression discards unwanted baseline samples while allowing the reconstruction of spike samples. The IC features a 32x32 MEA with 36μm pixel pitch and consumes 268nW per pixel from a single 1V supply. It achieves 9.8μVRMS input-referred noise and 0.3-5kHz bandwidth, resulting in NEF/PEF of 3.7/14.1.

KeywordBrain Compression Interface Neural Recording
DOI10.23919/VLSITechnologyandCir57934.2023.10185288
URLView the original
Indexed ByEI
Scopus ID2-s2.0-85167605665
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Citation statistics
Document TypeConference paper
CollectionINSTITUTE OF MICROELECTRONICS
Faculty of Science and Technology
Corresponding AuthorDante G. Muratore
Affiliation1.University of Macau
2.Stanford University
3.Yonsei University
4.TU Delft
Recommended Citation
GB/T 7714
MoonHyung Jang,Wei-Han Yu,Changuk Lee,et al. A 1024-Channel 268 nW/pixel 36x36 μm2/ch Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces[C], IEEE Xplore:IEEE, 2023.
APA MoonHyung Jang., Wei-Han Yu., Changuk Lee., Maddy Hays., Pingyu Wang., Nick Vitale., Pulkit Tandon., Pumiao Yan., Pui-In Mak., Youngcheol Chae., E.J. Chichilnisky., Boris Murmann., & Dante G. Muratore (2023). A 1024-Channel 268 nW/pixel 36x36 μm2/ch Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces. IEEE Symposium on VLSI Technology and Circuits, 2023-June.
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