The DOI matches its Crossref record; 0 of the 5 references it cites were found in a registry or an index.
An implantable 700μW 64-channel neuromodulation IC for simultaneous recording and stimulation with rapid artifact recovery
Open accessAbstract
We present an 180nm HV CMOS IC for concurrent neural stimulation and recording that combines 64 low-noise recording front-ends and 4 independent stimulators multiplexed to any of the 64 channels. The stimulators have 5mA peak current, 12V compliance and dynamic power management to maximize efficiency. Co-design of the stimulation and recording subsystems resulted in 100mV of recording linear range, 70nV/rtHz noise, and a rapid 1ms (single-sample) artifact recovery during stimulation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“A common solution is to prevent amplifier saturation via a wide linear input range [194].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [194]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Techniques such as adaptive filtering methods rely on the least-mean-squares (LMS) algorithm to implement infinite impulse response (IIR) and finite impulse response (FIR) digital filters to address and cancel out individual samples of artifacts [132], [194].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [194]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Untitled work
shulyzki (2015) · IEEE TBioCAS
UnresolvedUntitled work
rhew (2014) · IEEE JSSC
UnresolvedUntitled work
lo (2016) · IEEE ISSCC
UnresolvedUntitled work
muller (2015) · IEEE JSSC
UnresolvedUntitled work
sun (2014) · Neurotherapeutics
Unresolved
Flagged first, then by citations
5 of the 5 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
Moin, A., Zhou, A., Rahimi, A., Menon, A., Benatti, S., Alexandrov, G., Tamakloe, S., Ting, J., Yamamoto, N. A. D., Khan, Y., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-00510-8 · Cited by 698
Self-citationPiech, D. K., Johnson, B. C., Shen, K., Ghanbari, M. M., Li, K. Y., Neely, R. M., Kay, J. E., Carmena, J. M., Maharbiz, M. M., & Muller, R. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0518-9 · Cited by 442
Self-citationZhou, A., Santacruz, S. R., Johnson, B. C., Alexandrov, G., Moin, A., Burghardt, F., Rabaey, J. M., Carmena, J. M., & Muller, R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0323-x · Cited by 233
Self-citationZhang, M., Tang, Z., Liu, X., & Van der Spiegel, J. (2020) · Nature Electronics · doi:10.1038/s41928-020-0390-3 · Cited by 231
A Sub-mm 3 Ultrasonic Free-Floating Implant for Multi-Mote Neural Recording
Ghanbari, M. M., Piech, D. K., Shen, K., Alamouti, S. F., Yalcin, C., Johnson, B. C., Carmena, J. M., Maharbiz, M. M., & Muller, R. (2019) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2019.2936303 · Cited by 137
“When the signal is downmodulated, the kT/C noise is converted into out-of-band chopper ripple [23].”
BackgroundSelf-citation
Metadata
- Authors
- Johnson, B. C., Gambini, S., Izyumin, I., Moin, A., Zhou, A., Alexandrov, G., Santacruz, S. R., Rabaey, J. M., Carmena, J. M., & Muller, R.
- Affiliations
- University of California, Berkeley (US)
- ORCID
- Venue
- 2017 Symposium on VLSI Circuits
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- C48-C49
- ISSN
- 2166-9600 (ISSN-L)
- OpenAlex
- W2742939082
- Cited by
- 119
- References
- 5
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 10 · 2023: 10 · 2022: 12 · 2021: 16 · 2020: 15 · 2019: 16 · 2018: 18 · 2017: 1
- FWCI
- 27.15
- Type
- conference paper
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Symposium on VLSI Circuits (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radio Frequency Integrated Circuit Design
- Keywords
- neurostimulation, artifact recovery, low-power CMOS, HV-CMOS, dynamic power management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
