An Optimized Pipelined-Subranging ADC Architecture
Bibliographic record
Abstract
This paper reports an optimized pipelined-subranging ADC architecture that features new design techniques, such as, multiple MDACs, multiple open-loop residue amplifiers, relative comparison method and closed-loop circular resistive interpolation network. Multiple MDACs and multiple residue amplifier relaxes the linearity requirement down to the level that can be readily handled by the open-loop structure for fast settling while maintaining low power consumption. Relative comparison method helps to suppress the gain error caused by the inaccurate open-loop gain. The trade-off between speed and accuracy is broken by the circular resistive interpolation network. The new ADC architecture is successfully verified in design of a 12bit 100Msps pipelined-subranging ADC in commercial 0.35μm CMOS technology with the following specifications achieved: 2Vpp differential input range, ±0.6LSB DNL, 70dB SFDR, 62dB SNDR, ± 2% FS gain error, power dissipation of 520mW and a die size of 9mm2
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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 | 0.000 |
| 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) | 0.004 | 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".