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A 4-Channel VTC Peripheral Nerve Interface with 1.7 NEF and 50 mm-deep 600kbps Time-Encoded Wireless Galvanic Link

  • Boise State University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a low-power, 4-channel Voltage-to-Time Converter (VTC) Analog Front-End (AFE) designed for implantable electroneurogram (ENG) monitoring, fabricated in a 180 nm CMOS process. With a compact footprint of 0.23 mm2 and a power consumption of 11.22 μ W(2.8 μ W per channel), the AFE achieves an input-referred noise of 2.2 μ Vrms, enabling highfidelity neural signal acquisition. The system integrates a galvanic impulse link for energy-efficient data transmission through deep tissue, supporting data rates exceeding 600kbps without requiring an on-chip ADC. Time-domain encoding ensures charge-balanced operation for safety, while robust signal reconstruction mitigates tissue-induced attenuation and misalignment effects. Validated through bench-top experiments, ex vivo porcine tests, and human tissue phantom studies, the proposed system demonstrates minimal signal distortion and reliable performance across heterogeneous tissue, making it a promising solution for real-time, closedloop neuromodulation in compact, wirelessly powered implants.

Original languageEnglish
Title of host publicationProceedings - 21st IEEE Biomedical Circuits and Systems, BioCAS 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages404-408
Number of pages5
ISBN (Electronic)9798331573362
DOIs
StatePublished - 2025
Event21st IEEE Biomedical Circuits and Systems, BioCAS 2025 - Abu Dhabi, United Arab Emirates
Duration: 16 Oct 202518 Oct 2025

Publication series

NameProceedings - 21st IEEE Biomedical Circuits and Systems, BioCAS 2025

Conference

Conference21st IEEE Biomedical Circuits and Systems, BioCAS 2025
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period16/10/2518/10/25

Keywords

  • Analog Front-End
  • Electroneurogram
  • Galvanic Communication
  • Implantable Devices
  • Low-Power Electronics
  • Neuromodulation
  • Time-Domain Encoding
  • Tissue Phantom
  • Voltage-to-Time Converter
  • Wireless Neural Interfaces

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