Power Bound Analysis of a Two-Step MASH Incremental ADC Based on Noise-Shaping SAR ADCs
Bibliographic record
Abstract
Power consumption is an important limitation in designing analog-to-digital converters (ADCs) used in low-power sensing applications. This paper estimates analytically the power bound of a two-step multi-stage noise-shaping successive-approximation-register incremental ADC (two-step MASH NS-SAR IADC) proposed in our previous work. Our model considers the impacts of thermal noise, mismatch, and CMOS process (minimum feature size in CMOS technologies) on the power bounds of the proposed IADC. The analytic results show that thermal noise and CMOS process requirements determine the power consumption lower bounds in high and low resolutions, respectively. A comparison with the most competitive single-loop delta-sigma (ΔΣ) IADC shows a 3-dB higher theoretical figure-of-merit (FoM) for our proposed IADC when the resolutions are higher than 12-bit. Our proposed systematic analysis can be used to estimate the power bounds of amplifier-based NS-SAR ADCs used in either ΔΣ or incremental mode with multi-stage and multi-step topologies designed in various CMOS technologies. The reported analytic results are confirmed by experimental results of previously reported implementations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".