A Highly-Scalable DC-Coupled Direct-ADC Neural Recording Channel Architecture with Input-Adaptive Resolution

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dc.contributor.advisorKassiri, Hossein
dc.contributor.authorSayedi, Sayedeh Mina
dc.date.accessioned2022-12-14T16:42:24Z
dc.date.available2022-12-14T16:42:24Z
dc.date.copyright2022-09-28
dc.date.issued2022-12-14
dc.date.updated2022-12-14T16:42:24Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractThis thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 92×92µm for the implemented digital-backend.
dc.identifier.urihttp://hdl.handle.net/10315/40778
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectElectrical engineering
dc.subjectBiomedical engineering
dc.subject.keywordsNeural recording
dc.subject.keywordsEnergy efficiency
dc.subject.keywordsCompressive sensing
dc.subject.keywordsAdaptive sampling
dc.subject.keywordsAdaptive resolution
dc.subject.keywordsNeural ADC
dc.titleA Highly-Scalable DC-Coupled Direct-ADC Neural Recording Channel Architecture with Input-Adaptive Resolution
dc.typeElectronic Thesis or Dissertation

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