Digitally-enhanced high-order ΔΣ modulators
Bibliographic record
Abstract
The input feedforward path in a DeltaSigma modulator is an attractive technique for low-distortion swing-reduction design. It helps lower the power dissipation, especially in DeltaSigma modulators designed with low oversampling ratios (OSRs) in low-voltage nanometer CMOS technologies. However, a DeltaSigma modulator with analog feedforward (AFF) requires an analog adder before the quantizer, which can limit the achievable resolution or degrade the signal swing and increase the power dissipation. In this paper, a single-stage multibit DeltaSigma modulator with digital feedforward (DFF) is proposed to realize a high-order finite-impulse-response noise transfer function, thereby achieving high signal-to-quantization-noise ratios at low OSRs. Its key features include reduced swing at the opamp outputs, reduced sensitivity to integrator nonlinearities, and robustness to DeltaSigma modulator coefficient variations, all of which are achieved using only minimal additional digital hardware. Behavioral simulation results confirm that the proposed DFF modulator achieves the swing-reduction and low-distortion performance of an AFF modulator, while eliminating the need for an analog adder.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".