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김성진

Kim, Seong-Jin
Bio-inspired Advanced Sensors Lab.
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dc.citation.endPage 273 -
dc.citation.number 2 -
dc.citation.startPage 263 -
dc.citation.title IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS -
dc.citation.volume 18 -
dc.contributor.author Wu, Jiajia -
dc.contributor.author Akinin, Abraham -
dc.contributor.author Somayajulu, Jonathan -
dc.contributor.author Lee, Min S -
dc.contributor.author Paul, Akshay -
dc.contributor.author Lu, Hongyu -
dc.contributor.author Park, Yongjae -
dc.contributor.author Kim, Seong-Jin -
dc.contributor.author Mercier, Patrick P -
dc.contributor.author Cauwenberghs, Gert -
dc.date.accessioned 2024-03-04T14:05:12Z -
dc.date.available 2024-03-04T14:05:12Z -
dc.date.created 2024-02-26 -
dc.date.issued 2024-04 -
dc.description.abstract Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz–1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μ V rms over 0.001Hz–1Hz) and 435 MΩ input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion. -
dc.identifier.bibliographicCitation IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, v.18, no.2, pp.263 - 273 -
dc.identifier.doi 10.1109/TBCAS.2024.3368068 -
dc.identifier.issn 1932-4545 -
dc.identifier.scopusid 2-s2.0-85186991100 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/81514 -
dc.identifier.wosid 001196728500016 -
dc.language 영어 -
dc.publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC -
dc.title A Low-Noise Low-Power 0.001Hz–1kHz Neural Recording System-on-Chip with Sample-Level Duty-Cycling -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Engineering, Biomedical;Engineering, Electrical & Electronic -
dc.relation.journalResearchArea Engineering -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor biopotential recording -
dc.subject.keywordAuthor delta-sigma ADC -
dc.subject.keywordAuthor EEG -
dc.subject.keywordAuthor EGG -
dc.subject.keywordAuthor low-noise -
dc.subject.keywordAuthor low-power -
dc.subject.keywordAuthor Multichannel -
dc.subject.keywordAuthor sample-level duty-cycling -
dc.subject.keywordAuthor wideband -

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