Evidence map›Paper›PMID 38392030›Full record

ArticleBiosensors2024

Power-to-Noise Optimization in the Design of Neural Recording Amplifier Based on Current Scaling, Source Degeneration Resistor, and Current Reuse.

Zhen Wang, Xiao Wang, Guijun Shu, Meng Yin, Shoushuang Huang, Ming Yin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zhen WangState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou 570100, China.
Xiao WangState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou 570100, China.ORCID 0000-0001-5546-5647
Guijun ShuState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou 570100, China.
Meng YinState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou 570100, China.ORCID 0009-0008-4878-9991
Shoushuang HuangState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou 570100, China.
Ming YinState Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou 570100, China.

Funding

Key R&D Project of Hainan Province ZDYF2021SHFZ083Major Science and Technology Projects of Hainan Province ZDKJ2021032National Natural Science Foundation of China 32160204STI 2030-Major Projects 2022ZD0208602
6 · The paper itself

Abstract

This article presents the design of a low-power, low-noise neural signal amplifier for neural recording. The structure reduces the current consumption of the amplifier through current scaling technology and lowers the input-referred noise of the amplifier by combining a source degeneration resistor and current reuse technologies. The amplifier was fabricated using a 0.18 μm CMOS MS RF G process. The results show the front-end amplifier exhibits a measured mid-band gain of 40 dB/46 dB and a bandwidth ranging from 0.54 Hz to 6.1 kHz; the amplifier's input-referred noise was measured to be 3.1 μVrms, consuming a current of 3.8 μA at a supply voltage of 1.8 V, with a Noise Efficiency Factor (NEF) of 2.97. The single amplifier's active silicon area is 0.082 mm

Indexed as

Amplifiers, ElectronicSignal Processing, Computer-AssistedEquipment Designcurrent reusecurrent scalingneural signal amplifiersource degeneration resistors

Identifiers

PMID38392030
PMCPMC10887131

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.