Optimization of Delta-Sigma ADC for Column-Level Data Conversion in CMOS Image Sensors
Bibliographic record
Abstract
A delta-sigma analog-to-digital-converter (ADC) is designed, optimized and simulated for column-level data conversion in a CMOS image sensor. For a 0.18μm process, the design achieves 80dB of signal-to-noise ratio (SNR), including a 10dB margin for kTC noise not simulated, and consumes 210μW of power at a 50kHz sampling rate. Low power is realized mainly by using a first-order architecture and minimizing the capacitors. For the modulator, a boosted-folded-cascode operational transconductance amplifier (OTA) is optimized to achieve a gain of 90dB with a unity-gain bandwidth of 300MHz. The decimator is also optimized by placing part of the circuit at the chip level. Zero distortion is possible in the decimator due to the discrete-time nature of the input signal. The proposed ADC allows a reduction in the read-out nonlinearity of a CMOS image sensor, enabling a high SNR to be realized.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".