Evidence map›Paper›PMID 42293814›Full record

ArticleIEEE sensors reviews2025

Review of In-Pixel Gain Amplifiers and Readout Circuits for Low-Light CMOS Image Sensors.

Amr M Maghraby, Ibrahim Bozyel, Islam T Abougindia, Suat U Ay

Abstract read
In one paragraph

Article in IEEE sensors reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

4 authors.

Amr M MaghrabyElectrical and Computer Engineering Department, University of Idaho, Moscow, ID 83843 USA.ORCID 0009-0007-4772-3109
Ibrahim BozyelElectrical and Computer Engineering Department, Worcester Polytechnique Institute, Worcester, MA 01609 USA.ORCID 0000-0002-2841-7890
Islam T AbougindiaElectronics Engineering Department, Military Technical College, Cairo 11371, Egypt.ORCID 0009-0007-6238-3982
Suat U AyElectrical and Computer Engineering Department, University of Idaho, Moscow, ID 83843 USA.ORCID 0000-0001-7640-4253

Funding

SCH: Machine LEarning & MicrofluiDics for Multimodal Sensing of TiCk-bOrne Diseases(MEDICO)R01AI174300 · NIAID · WEST VIRGINIA UNIVERSITY · PI SRIVASTAVA, SOUMYA K · 2022 to 2025
$1.2M
NIAID NIH HHS R01 AI174300
6 · The paper itself

Abstract

The low-light performance of image sensors can be enhanced by designing high-effective conversion gain signal paths from photon conversion sites to chip outputs. This can be achieved by using high-gain in-pixel amplifiers, high-gain column amplifiers, and by reducing the conversion capacitance of photon-sensing nodes. However, each of these approaches presents unique challenges and limitations that have restricted their widespread adoption in low-light applications. This article reviews the use of in-pixel gain amplifiers and their signal chain electronics in high-conversion-gain complementary metal-oxide-semiconductor (CMOS) image sensors over the past two decades. In-pixel gain amplifiers are classified into different categories according to the type of amplification technique used. Analyses of the column-referred conversion gain and the noise of each topology are presented alongside the different metrics used to characterize CMOS image sensor pixels for low-light imaging applications. The performance metrics of various in-pixel gain amplifiers are compared, providing a framework that highlights the best achieved input-referred noise in CMOS image sensors over the past 15 years. Furthermore, different tradeoffs are examined between optimizing conversion gain, pixel full-well capacity, and input read noise in both voltage and charge domains.

Indexed as

Conversion gain (CG)in-pixel amplifierslow-noise readout integrated circuit (ROIC)Readout integrated circuit (ROIC)

Identifiers

PMID42293814
PMCPMC13264286

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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.