Tradeoffs in imager design parameters for sensor reliability
Bibliographic record
Abstract
Image sensors are continuously subject to the development of in-field permanent defects in the form of hot pixels. Based on measurements of defect rates in 23 DSLRs, 4 point and shoot cameras, and 11 cell phone cameras, we show in this paper that the rate of these defects depends on the technology (APS or CCD) and on design parameters the like of imager area, pixel size, and gain (ISO). Increasing the image sensitivity (ISO) (from 400 up to 25,600 ISO range) causes the defects to be more noticeable, with some going into saturation, and at the same time increases the defect rate. Partially stuck hot pixels, which have an offset independent of exposure time, make up more than 40% of the defects and are particularly affected by ISO changes. Comparing different sensor sizes has shown that if the pixel size is nearly constant, the defect rate scales with sensor area. Plotting imager defect/year/sq mm with different pixel sizes (from 7.5 to 1.5 microns) and fitting the result shows that defect rates grow rapidly as pixel size shrinks, with an empirical power law of the pixel size to the -2.5. These defect rate trends result in interesting tradeoffs in imager design.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".