Analysis and Design of a Pipelined MASH Continuous-Time Delta-Sigma Modulator With 15.4 MHz-BW and 82.6 dB-SNDR
Bibliographic record
Abstract
This paper presents the design of a wideband pipelined multi-stage noise shaping (MASH) continuous time (CT) delta-sigma modulator (DSM). The quantization error of the overall$1^{\mathrm {st}}$-stage DSM is extracted as the input of the$2^{\mathrm {nd}}$stage, while the outputs of both stages are simply combined without using any digital filters. Overall, different shaping functions are generated for both QN without requiring any digital QN cancellation. Therefore, the pipelined MASH (PMASH) significantly mitigates QN leakage while retaining the decent loop stability of a traditional MASH. Additionally, several analyses have been made for the PMASH topology, e.g. the design guideline, the signal transfer function (STF), the robustness, etc. Clocked at 800MHz and enabling on-chip DAC calibration, the 65nm CMOS prototype with an exemplary 2-2 topology using multi-bit quantizers achieves 82.6 dB SNDR, 98.8 dB SFDR over 15.4 MHz BW at −0.5 dBFS 1.8 MHz input. The power consumption is 16.9 mW with 1.2V/1.5V supplies. It results in a competitive FoM${}_{\mathrm {S\vert SNDR}}$of 172.2 dB, while it avoids any off-chip calibrations.
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 | 0.000 | 0.000 |
| 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".