Oversampling ADC: A Review of Recent Design Trends
Bibliographic record
Abstract
Oversampling analog-to-digital converters (ADC) serve as the backbone of high-performance, high-precision data interfaces, owing to their remarkable ability to filter out quantization noise. This attribute makes them the preferred choice for applications requiring high signal-to-noise ratio (SNR) and moderate bandwidth, with great design flexibility. This paper provides an extensive survey of the latest advancements in oversampling ADC tailored for such applications as documented in recent literature. Specifically focusing on design techniques employed within the last five years, the survey encompasses various oversampling ADC architectures, including discrete-time and continuous-time$\Delta \Sigma $, noise-shaping SAR, zoom, incremental, and time-domain modulators. A thorough performance comparison between these different topologies is presented, highlighting designs that achieve the best figures-of-merit. Furthermore, the paper explores circuit-level design trends commonly shared among these architectures, with particular attention given to amplifier designs for loop filters. Conclusions drawn highlight the limitations of much of the research works in the context of implementing ADC within complete systems, while also providing insight into the expected future trends that will shape the field moving forward.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".