Cargando…

Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions

A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS), assuming stationary conditions was recently presented in this journal. In this study, we extend the stochastic approach to non-stationary conditions. Non-stat...

Descripción completa

Detalles Bibliográficos
Autores principales: Cherniak, Gil, Nemirovsky, Jonathan, Nemirovsky, Amikam, Nemirovsky, Yael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490096/
https://www.ncbi.nlm.nih.gov/pubmed/37687800
http://dx.doi.org/10.3390/s23177344
_version_ 1785103762571919360
author Cherniak, Gil
Nemirovsky, Jonathan
Nemirovsky, Amikam
Nemirovsky, Yael
author_facet Cherniak, Gil
Nemirovsky, Jonathan
Nemirovsky, Amikam
Nemirovsky, Yael
author_sort Cherniak, Gil
collection PubMed
description A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS), assuming stationary conditions was recently presented in this journal. In this study, we extend the stochastic approach to non-stationary conditions. Non-stationary conditions occur in gated imaging applications. This new stochastic model, which is based on fundamental physical considerations, enlightens us with new insights into gated CMOS imaging, regardless of the sensor. The Signal-to-Noise Ratio (SNR) is simulated, allowing optimized performance. The conversion gain should be determined under stationary conditions.
format Online
Article
Text
id pubmed-10490096
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104900962023-09-09 Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions Cherniak, Gil Nemirovsky, Jonathan Nemirovsky, Amikam Nemirovsky, Yael Sensors (Basel) Communication A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS), assuming stationary conditions was recently presented in this journal. In this study, we extend the stochastic approach to non-stationary conditions. Non-stationary conditions occur in gated imaging applications. This new stochastic model, which is based on fundamental physical considerations, enlightens us with new insights into gated CMOS imaging, regardless of the sensor. The Signal-to-Noise Ratio (SNR) is simulated, allowing optimized performance. The conversion gain should be determined under stationary conditions. MDPI 2023-08-23 /pmc/articles/PMC10490096/ /pubmed/37687800 http://dx.doi.org/10.3390/s23177344 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Cherniak, Gil
Nemirovsky, Jonathan
Nemirovsky, Amikam
Nemirovsky, Yael
Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions
title Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions
title_full Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions
title_fullStr Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions
title_full_unstemmed Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions
title_short Modeling Signal-to-Noise Ratio of CMOS Image Sensors with a Stochastic Approach under Non-Stationary Conditions
title_sort modeling signal-to-noise ratio of cmos image sensors with a stochastic approach under non-stationary conditions
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490096/
https://www.ncbi.nlm.nih.gov/pubmed/37687800
http://dx.doi.org/10.3390/s23177344
work_keys_str_mv AT cherniakgil modelingsignaltonoiseratioofcmosimagesensorswithastochasticapproachundernonstationaryconditions
AT nemirovskyjonathan modelingsignaltonoiseratioofcmosimagesensorswithastochasticapproachundernonstationaryconditions
AT nemirovskyamikam modelingsignaltonoiseratioofcmosimagesensorswithastochasticapproachundernonstationaryconditions
AT nemirovskyyael modelingsignaltonoiseratioofcmosimagesensorswithastochasticapproachundernonstationaryconditions