A new approach to the design of low-sensitivity high-resolution bandpass Σ-Δ A/D converters
Bibliographic record
Abstract
It is well known that /spl Sigma/-/spl Delta/ A/D converters having complementary signal and noise transfer functions exhibit high signal-to-quantization-noise ratios (SQNRs) and high dynamic ranges (DRs), but that they tend to be overly sensitive to capacitor mismatches (potentially leading to unstable converter operation) in a corresponding switched-capacitor (SC) hardware implementation. Recently, a new class of /spl Sigma/-/spl Delta/ A/D converters was developed based on magnitude-squared or magnitude complementary signal and noise transfer functions to decrease the sensitivity to capacitor mismatches at the expense of slightly lower SQNRs and DRs. This paper is concerned with the development of a novel approach to the design of /spl Sigma/-/spl Delta/ A/D converters. This approach combines the transfer function complementarity (in the overall frequency band) with signal and noise transfer function magnitude complementarity (in the signal passband) to obtain a single /spl Sigma/-/spl Delta/ A/D converter with a cascade-of-resonators configuration. Consequently, the resulting A/D converters achieve not only high-stability (arising from magnitude complementarity), but also high DR and SQNR (arising from transfer function complementarity) in the SC hardware implementation. A practical application example is given to illustrate the results.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".