A variable topology partitioned pixel amplifier for low and high light level detection in a CMOS imager
Bibliographic record
Abstract
In this work the concept of using a single partitioned pixel amplifier with variable topology for both low light level operation and Wide Dynamic Range (WDR) for CMOS imagers is presented. Low light level imaging is based on the Active Reset (AR) technique combined with the Active Column Sensor (ACS) readout technique for low noise operation. WDR for high light level detection is achieved by utilizing multiple resets via real-time feedback where each pixel in the field of view is independent and can automatically set an exposure time according to its illumination. Due to the commonalities in the low and high light level readout techniques, and the fact that they occur in staggered instances of time, we propose the use of a single column level partitioned pixel amplifier which can be configured in various modes of operation to satisfy the conditions for the suggested techniques. We also propose a Conditional Active Reset (CAR) scheme in which an AR is employed for every reset in the multiple-reset WDR algorithm, thereby reducing the overall noise involved in the technique. The advantages of the proposed column level partitioned pixel amplifier are simplicity in the analog readout path, reduced chip size and less power consumption. The Variable Topology Amplifier was designed and simulated in a mixed signal 0.18 um CMOS technology. Its design is discussed and simulation results are presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".