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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".