Revisiting the Modeling of the Conversion Gain of CMOS Image Sensors with a New Stochastic Approach.

CMOS Image Sensor conversion gain global shutter low read noise photon counting pinned photodiode shot noise

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
08 Oct 2022
Historique:
received: 15 09 2022
revised: 02 10 2022
accepted: 06 10 2022
entrez: 14 10 2022
pubmed: 15 10 2022
medline: 15 10 2022
Statut: epublish

Résumé

A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal-oxide-semiconductor (CMOS) image sensors (APS) with at least four transistors is presented. This model, based on the fundamental principles of electronic noise, may provide a reliable calibration of the gain conversion, which is one of the most important parameters of CMOS Image Sensor pixels. The new model revisits the "gold standard" ratio method of the measured variance of the shot noise to the mean value. The model assumes that shot noise is the dominant noise source of the pixel. The microscopic random time-dependent voltage of any shot noise electron charging the junction capacitance C of the sensing node may have either an exponential form or a step form. In the former case, a factor of 1/2 appears in the variance to the mean value, namely, q/2C is obtained. In the latter case, the well-established ratio q/C remains, where q is the electron charge. This correction factor affects the parameters that are based on the conversion gain, such as quantum efficiency and noise. The model has been successfully tested for advanced image sensors with six transistors fabricated in a commercial FAB, applying a CMOS 180 nm technology node with four metals. The stochastic modeling is corroborated by measurements of the quantum efficiency and simulations with advanced software (Lumerical).

Identifiants

pubmed: 36236717
pii: s22197620
doi: 10.3390/s22197620
pmc: PMC9570612
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : "Smart Imaging" Consortium Israel Innovation Authority
ID : 74391

Références

Philos Trans A Math Phys Eng Sci. 2014 Feb 24;372(2012):20130100
pubmed: 24567470

Auteurs

Gil Cherniak (G)

Electrical Engineering Department, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

Amikam Nemirovsky (A)

Department of Electrical Engineering, Kinneret College on the Sea of Galilee, Tzemah 1513200, Israel.

Yael Nemirovsky (Y)

Electrical Engineering Department, Technion-Israel Institute of Technology, Haifa 3200003, Israel.

Classifications MeSH