A Compact Low-Power Chopper Low Noise Amplifier for High Density Neural Front-Ends.

Alessandro Fava, Francesco Centurelli, Pietro Monsurrò, Giuseppe Scotti

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

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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.

Novel Tunable Pseudoresistor-Based Chopper-Stabilized Capacitively Coupled Amplifier and Its Machine Learning-Based Application.Micromachines · 2025
Article

4 · The record

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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

4 authors.

Alessandro FavaDepartment of Information, Electronics and Telecommunication Engineering, Sapienza University of Rome, 00184 Roma, Italy.ORCID 0000-0002-5454-9643

Francesco CenturelliDepartment of Information, Electronics and Telecommunication Engineering, Sapienza University of Rome, 00184 Roma, Italy.ORCID 0000-0003-3880-2546

Pietro MonsurròDepartment of Information, Electronics and Telecommunication Engineering, Sapienza University of Rome, 00184 Roma, Italy.ORCID 0000-0002-3821-6566

Giuseppe ScottiDepartment of Information, Electronics and Telecommunication Engineering, Sapienza University of Rome, 00184 Roma, Italy.ORCID 0000-0002-5650-8212

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a low-power and area-efficient chopper-stabilized low noise amplifier (CS-LNA) for in-pixel neural recording systems. The proposed CS-LNA can be used in a multi-channel architecture, in which the chopper mixers of the LNA are exploited to provide the time division multiplexing (TDM) of several channels, while reducing the flicker noise and rejecting the Electrode DC Offset (EDO). A detailed noise analysis including the effect of the chopper stabilization on flicker noise, and a design flow to optimize the trade-off between input-referred noise and silicon area are presented, and utilized to design the LNA. The adopted approach to reject the EDO allows to tolerate an input offset of ±50 mV, without appreciably affecting the CS-LNA performance, and does not require an additional DC Servo Loop (DSL). The proposed CS-LNA has been fabricated in a 0.13 μm CMOS process with an area of 0.0268 mm

Indexed as

analog front-endbio-amplifierchoppinglow noise amplifierneural recording

Identifiers

PMID40006386

DOI10.3390/s25041157

PMCPMC11859806

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LicenceCC BY

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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.