High-Order Multibit Modulators and Pseudo Data-Weighted-Averaging in Low-Oversampling>tex<$Delta Sigma$>/tex<ADCs for Broad-Band Applications
Bibliographic record
Abstract
High-speed high-resolution /spl Delta//spl Sigma/ analog-to-digital converters (ADCs) for broad-band communication applications must be designed at a low oversampling ratio (OSR). However, lowering the OSR limits the efficiency of a /spl Delta//spl Sigma/ ADC in achieving a high-resolution A/D conversion. This paper presents several techniques that enable the OSR reduction in /spl Delta//spl Sigma/ ADCs without compromising the resolution. 1) Noise transfer function (NTF). In this paper, a single-stage multibit /spl Delta//spl Sigma/ modulator with a high-order finite-impulse-response NTF is proposed to achieve high signal-to-quantization-noise ratios at low OSRs. Its key features include: decreased circuit complexity, improved robustness to modulator coefficient variations, and reduced sensitivity to integrator nonlinearities. Its performance is validated through behavioral simulations and compared to traditional /spl Delta//spl Sigma/ modulator structures. 2) Signal transfer function (STF). This paper describes how the STF of a /spl Delta//spl Sigma/ modulator can be designed, independently of the NTF, in order to significantly reduce the harmonic distortion due to opamp nonidealities and to help lower the power dissipation. 3) Dynamic element matching (DEM) is also presented. Data weighted averaging (DWA) has prevailed as the most practical DEM technique to linearize the internal digital-to-analog converter (DAC) of a multibit /spl Delta//spl Sigma/ modulator, especially when the number of DAC elements is large. However, the occurrence of in-band signal-dependent tones, when using DWA at a low OSR, degrades the spurious-free dynamic range. This paper proposes a simple technique, called pseudo DWA, to solve the DWA tone problem without sacrificing the signal-to-noise ratio. Its implementation adds no extra delay in the /spl Delta//spl Sigma/ feedback loop and requires only minimal additional digital hardware. Existing schemes for DWA tone reduction are also compared.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".