Small-area decimators for delta-sigma video sensors
Bibliographic record
Abstract
A delta-sigma, or sigma-delta, analog-to-digital converter (ADC) comprises both a modulator, which implements oversampling and noise shaping, and a decimator, which implements low-pass filtering and downsampling. Whereas these ADCs are ubiquitous in audio applications, their usage in video applications is emerging. Because of oversampling, it is preferable to integrate delta-sigma ADCs at the pixel level of megapixel video sensors. Moreover, with pixel-level applications, area usage per ADC is much more important than with chip-level applications, where there is only one or a few ADCs per chip. Recently, a small-area decimator was presented that is suitable for pixel-level applications. However, though the pixel-level design is small enough for invisible-band video sensors, it is too large for visible-band ones. As shown here, nanoscale CMOS processes offer a solution to this problem. Given constant specifications, small-area decimators are designed, simulated, and laid out, full custom, for 180, 130, and 65nm standard CMOS processes. Area usage of the whole decimator is analyzed to establish a roadmap for the design and demonstrate that it could be competitive compared to other digital pixel sensors, based on Nyquist-rate ADCs, that are being commercialized.
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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 | 0.001 |
| 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 | 0.001 |
| 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".