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Record W2048485090 · doi:10.1109/ted.2012.2205690

Analysis of Dynamic Range, Linearity, and Noise of a Pulse-Frequency Modulation Pixel

2012· article· en· W2048485090 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2012
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsYork University
FundersCMC Microsystems
KeywordsLinearityComparatorCMOSPixelElectronic engineeringDynamic rangeFixed-pattern noiseNoise (video)Image sensorBandwidth (computing)Computer scienceElectrical engineeringEngineeringTelecommunicationsArtificial intelligenceVoltageImage (mathematics)

Abstract

fetched live from OpenAlex

A complete pulse-frequency modulation (PFM) pixel design analysis and noise measurement for CMOS image sensor applications are presented. This work investigates the design parameters such as dynamic range (DR), signal linearity, and comparator characteristics. The design strategies for wide DR imaging are addressed in detail, and signal linearity is analyzed by considering the analog circuit parameters. The temporal noise is also measured to understand the design tradeoffs of the PFM pixels. The analysis is executed by performing HSPICE simulation and practical pixel measurements. The technology used by the measured pixel is a 0.18-μm one-poly six-metal CMOS process. According to the results, a PFM pixel using the submicrometer CMOS process has a DR of 130-160 dB, and the cost of reaching a higher signal linearity or lower noise floor is the loss of frame rate. In addition, the bandwidth of the comparator can be extended to improve sensor linearity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.236
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it