Design Techniques for Sample-and-Hold with Bootstrapping in Low-Power SAR ADC
Bibliographic record
Abstract
This paper investigates the errors of sample-and-hold (S/H) with bootstrapping in low-power SAR ADC. We show charge injection errors are of common-mode characteristics mainly and their impact is largely suppressed via differential signaling. We further show errors caused by bootstrapping voltage feed-through have both common-mode and differential-mode components. The impact of the former is suppressed by differential signaling while the latter doubles. Moreover, the loss of bootstrapping voltage due to the use of nMOS bootstrapping capacitors and the impact of the size of bootstrapping nMOS capacitors on bootstrapping voltage are investigated. Furthermore, we show errors caused by the voltage drop of sampling switches are of differential characteristics and double with differential signaling. The impact of supply voltage reduction on the voltage drop of sampling switches is also studied. We show the lower the supply voltage, the worse the voltage drop of sampling switches. Finally, we show errors caused by the voltage drop of sampling switches are most critical and can be reduced by increasing the size of sampling switches without sacrificing charge injection errors. These findings are verified using the simulation results of a differential S/H designed in TSMC 130 nm with a reduced supply voltage of 0.8 V.
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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) | 0.002 | 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".