The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
A 0.013 ${\hbox {mm}}^{2}$, 5 $\mu\hbox{W}$, DC-Coupled Neural Signal Acquisition IC With 0.5 V Supply
Abstract
We present an area-efficient neural signal-acquisition system that uses a digitally intensive architecture to reduce system area and enable operation from a 0.5 V supply. The architecture replaces ac coupling capacitors and analog filters with a dual mixed-signal servo loop, which allows simultaneous digitization of the action and local field potentials. A noise-efficient DAC topology and an compact, boxcar sampling ADC are used to cancel input offset and prevent noise folding while enabling “per-pixel” digitization, alleviating system-level complexity. Implemented in a 65 nm CMOS process, the prototype occupies 0.013 mm2while consuming 5 μW and achieving 4.9 μVrms of input-referred noise in a 10 kHz bandwidth.
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In the citing paper
“However, modern circuit designs [77, 92] and IC fabrication technologies [18, 86] have resulted in innovative designs, enabling designers to relax such compromises.”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [86]
In the citing paper
“CNS amplifiers have also been developed that can acquire both types of signals, separately filtering, amplifying and outputting each signal type [46, 86, 100].”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [86]
In the citing paper
“Recently, Muller et al. [86] introduced the power efficiency factor (PEF), which takes into account both the operating current and the supply voltage and therefore provides a better comparison of the amplifier’s performance.”
From Implantable neurotechnologies: a review of integrated circuit neural amplifiers (2016) · cites this paper as [86]
Citations
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A 0.013mm2 5μW DC-coupled neural signal acquisition IC with 0.5V supply
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Flagged first, then by citations
1 of the 27 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- xiao, 2010, A 20 <ref_formula><tex Notation="TeX">$\mu{W}$</tex> </ref_formula> neural recording tag with supply-current-modulated AFE in 0.13 <ref_formula> <tex Notation="TeX">$\mu{\hbox {m}}$</tex></ref_formula> CMOS, IEEE Solid-State Circuits Conf Dig Tech Papers, 122
- muller, 2011, A 0.013 <ref_formula><tex Notation="TeX">${\hbox {mm}}^{2}$</tex></ref_formula>, 5 <ref_formula><tex Notation="TeX">$\mu{W}$</tex> </ref_formula>, DC-coupled neural signal acquisition IC with 0.5 V supply, IEEE Solid-State Circuits Conf Dig Tech Papers, 302
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Revealing neuronal function through microelectrode array recordings
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A Minimally Invasive 64-Channel Wireless μECoG Implant
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Self-citationAn Implantable 455-Active-Electrode 52-Channel CMOS Neural Probe
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Design of Ultra-Low Power Biopotential Amplifiers for Biosignal Acquisition Applications
Zhang, F., Holleman, J. H., & Otis, B. P. (2012) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2177089 · Cited by 237
Metadata
- Authors
- Muller, R., Gambini, S., & Rabaey, J. M.
- Affiliations
- University of California, Berkeley (US)
- Venue
- IEEE Journal of Solid-State Circuits
- Published
- 2012-01
- Volume
- 47
- Issue
- 1
- Pages
- 232-243
- ISSN
- 0018-9200 (ISSN-L)
- 1558-173X
- OpenAlex
- W2003444784
- Cited by
- 338
- References
- 27
- Citations by year
- 2026: 11 · 2025: 23 · 2024: 18 · 2023: 21 · 2022: 22 · 2021: 26 · 2020: 26 · 2019: 25 · 2018: 34 · 2017: 28 · 2016: 25 · 2015: 26 · 2014: 26 · 2013: 20 · 2012: 7
- FWCI
- 10.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Solid-State Circuits (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radio Frequency Integrated Circuit Design
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- neural signal acquisition, low-power integrated circuits, local field potentials, digital-to-analog converter, noise folding, low-voltage operation, 65 nm CMOS, area-efficient circuit design
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