Noise analysis of fault tolerant active pixel sensors
Bibliographic record
Abstract
As digital imagers grow in pixel count and area, the ability to correct for pixel defects becomes more important. A fault tolerant active pixel sensor (APS) has previously been designed and fabricated that can correct for stuck high and stuck low defects. Analyses of the pixel noise for a standard APS and a fault tolerant APS are presented that consider reset noise, photocurrent shot noise, dark current shot noise, transistor thermal noise, transistor flicker noise, operational amplifier noise, and feedback resistor thermal noise. Under worst case conditions (no illumination), the noise of the fault tolerant APS is 1.106 /spl times/ more than a standard APS. At a typical illumination level, the fault tolerant APS noise is nearly unchanged to that of a standard APS. Previous research has shown that the fault tolerant APS is more sensitive than a standard APS, thus the overall signal-to-noise ratio of the fault tolerant APS should be greater than the standard APS except under very low light conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".