A 500 kHz-BW, 90 dB-SNDR Lean Noise-Canceling SMASH Delta-Sigma ADC in 65 nm CMOS
Bibliographic record
Abstract
This paper presents a novel$\Delta \Sigma$ADC architecture designed to meet stringent requirements for high SNDR at moderate bandwidths. Based on a sturdy MASH$\Delta \Sigma$with NSSAR second stage, the architecture achieves noise cancellation by virtue of the NS-SAR feedforward property. The NS-SAR stage also benefits from significantly reduced design constraints due to the additional noise shaping provided by the first stage, allowing a more power-efficient design. An innovative dynamic amplifier is proposed to optimize the design trade-off in the first integrator. Simulation results demonstrate excellent performance, achieving 500 kHz bandwidth and 90.3 dB SNDR with a power consumption of$\mathbf{1. 6 3 3} \mathbf{~ m W}$, yielding a$\mathbf{F O M}_{\mathbf{S c}}$of$\mathbf{1 7 5. 2} \mathbf{d B}$.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".