Evidence map›Paper›PMID 39771727›Full record

ArticleSensors (Basel, Switzerland)2024

A 35 nV/√Hz Analog Front-End Circuit with Adjustable Bandwidth and Gain in UMC 40 nm CMOS for Biopotential Signal Acquisition.

Lu Liu, Bin Wang, Yiren Xu, Xiaokun Lin, Weitao Yang, Yinglong Ding

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Lu LiuState Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China.
Bin WangState Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China.
Yiren XuState Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China.
Xiaokun LinState Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China.
Weitao YangState Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China.
Yinglong DingState Key Discipline Laboratory of Wide Bandgap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an 710071, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a 35 nV/√Hz analog front-end (AFE) circuitdesigned in the UMC 40 nm CMOS technology for the acquisition of biopotential signal. The proposed AFE consists of a capacitive-coupled instrumentation amplifier (CCIA) and a combination of a programmable gain amplifier (PGA) and a low-pass filter (LPF). The CCIA includes a DC servo loop (DSL) to eliminate electrode DC offset (EDO) and a ripple rejection loop (RRL) with self-zeroing technology to suppress high-frequency ripples caused by the chopper. The PGA-LPF is realized using switched-capacitor circuits, enabling adjustable gain and bandwidth. Implemented in theUMC 40 nm CMOS process, the AFE achieves an input impedance of 368 MΩ at 50 Hz, a common-mode rejection ratio (CMRR) of 111 dB, an equivalent input noise of 1.04 μVrms over the 0.5-1 kHz range, and a maximum elimination of 50 mV electrode DC offset voltage. It occupies an area of only 0.39 × 0.47 mm

Indexed as

bandwidth-gain adjustablebiopotential signal acquisitioncapacitively-coupled chopper instrumentation amplifierdc servo looplow noiseripple reduction loop

Identifiers

PMID39771727
PMCPMC11679665

What OpenQuestion holds

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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.