A 0.45 V 100-Channel Neural-Recording IC With Sub-<formula formulatype="inline"><tex Notation="TeX">$\mu {\rm W}$</tex></formula>/Channel Consumption in 0.18 <formula formulatype="inline"><tex Notation="TeX">$\mu{\rm m}$</tex></formula> CMOS
Abstract
Neural prosthetics and personal healthcare have increasing need of high channel density low noise low power neural sensor interfaces. The input referred noise and quantization resolution are two essential factors which prevent conventional neural sensor interfaces from simultaneously achieving a good noise efficiency factor and low power consumption. In this paper, a neural recording architecture with dynamic range folding and current reuse techniques is proposed and dedicated to solving the noise and dynamic range trade-off under low voltage low power operation. Measured results from the silicon prototype show that the proposed design achieves 3.2 μVrms input referred noise and 8.27 effective number of bits at only 0.45 V supply and 0.94 μW/channel power consumption.
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Amplifiers using the current-reuse topology achieve state-of-the-art noise performance.
“Amplifiers using this topology achieve state-of-the-art noise performance [49, 129, 156].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [49]
Han et al. used 0.9 V and 0.45 V supply domains for the amplification stage and ADC, respectively.
“Finally, the chip reported by Han et al. [48] used 0.9 and 0.45 V supply domains for the amplification stage and ADC, respectively.”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [48]
Continued shrinking of fine-line CMOS processes has helped increase channel counts and add capabilities such as on-chip analog-to-digital conversion and digital radio transmission.
“The steady reduction in fine-line CMOS technologies has contributed to increased channel counts [6, 77] and expanded capabilities, such as on-chip analog-to-digital conversion [49, 156] and digital radio transmission [16, 67].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [49]
The inadequacy of NEF alone becomes more pronounced because recent works have reported operating supply voltages below 1 V.
“This inadequacy becomes more pronounced, as recent works have reported operating supply voltages below 1 V [49, 129, 156].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [49]
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Self-citationHigh-density neural recording system design
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- Authors
- Han, D., Zheng, Y., Rajkumar, R., Dawe, G. S., & Je, M.
- Affiliations
- Nanyang Technological University (SG)
- National University of Singapore (SG)
- Venue
- IEEE Transactions on Biomedical Circuits and Systems
- Published
- 2013-12
- Volume
- 7
- Issue
- 6
- Pages
- 735-746
- ISSN
- 1932-4545 (ISSN-L)
- 1940-9990
- OpenAlex
- W2406533156
- Cited by
- 147
- References
- 38
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 7 · 2023: 7 · 2022: 17 · 2021: 13 · 2020: 12 · 2019: 15 · 2018: 16 · 2017: 13 · 2016: 18 · 2015: 11 · 2014: 8
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agency for Science, Technology and Research (A*STAR), Singapore, Science and Engineering Research Council (SERC) (102171 0162)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radio Frequency Integrated Circuit Design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- neural recording, low-power analog front-end, input-referred noise, current reuse, low-voltage operation, neuroprosthetics, 0.18 μm CMOS
- MeSH terms
- Animals, Diagnostic Techniques, Neurological, Electrodes, Implanted, Equipment Design, Semiconductors, Signal Processing, Computer-Assisted, Silicon, Rats, Sprague-Dawley, Rats, Micro-Electrical-Mechanical Systems, Neural Prostheses
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