A 10-MHz BW 77.3-dB SNDR 640-MS/s GRO-Based CT MASH ΔΣ Modulator
Bibliographic record
Abstract
We present in this brief a novel multi-stage noise-shaping (MASH) 3–1 continuous-time (CT) delta-sigma modulator ($\Delta \Sigma \text{M}$) with gated ring oscillator based quantizers (GROQs) in both stages of the cascade. The use of GROQs increases the linearity performance with respect to the conventional voltage controlled oscillator based quantizers (VCOQs) and allows a more robust extraction of the front-end stage quantization error in the time domain, thus making the proposed architecture more suitable to implement high-order expandable scaling-friendly MASH$\Delta \Sigma $Ms, in which the back-end stages are implemented by mostly-digital GRO-based time-to-digital converters (TDCs). The circuit has been fabricated in a 65-nm CMOS technology with 1-V supply voltage, and it operates at 640-MHz sampling frequency to digitize 10-MHz signals. To the best of the authors’ knowledge, this is the first reported experimental validation of a GRO-based CT MASH$\Delta \Sigma \text{M}$, featuring a 79.8-dB signal to noise ratio (SNR) at −2.2-dBFS, a 77.3-dB signal to (noise + distortion) ratio (SNDR) at −4-dBFS and a dynamic range (DR) of 81.7 dB, with a power consumption of 12-mW. These metrics demonstrate state-of-the-art performance with a DR-based Schreier FOM of 170.9 dB.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".