22.2 A 700MHZ-BW −164dBFS/Hz-Small-Signal-NSD 703mW Continuous-Time Pipelined ADC with On-Chip Digital Reconstruction Achieving <-85dBFS HD<sub>3</sub> using Digital Cancellation of DAC Errors
Bibliographic record
Abstract
High-performance instrumentation and communications demand an ADC technology push to achieve both – an ultra-low NSD of -164dBFS/Hz and HD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> /IM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> <−80dBFS – over a BW >500MHz with sub-Watt power dissipation. 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -order distortion matters since for modulated inputs it falls near the signal and limits EVM. Combined with ultra-low NSD, it ensures robustness to large interferers when detecting small signals. Pushing DT ADCs to such performance via impedance scaling imposes significant overhead in the driver and antialiasing filter (AAF). E.g. [1] would need to be ~25×-parallelized to reach the NSD (but would still violate HD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> ), requiring a large ~27pF load to be driven at 900MHz BW with low NSD, HD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> , and kickback. AAF design, too, is not easy due to tradeoffs between order, insertion loss, and group delay. CT ΔΣ ADCs are more system-friendly since their resistive input impedance (Z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</inf> ) and implicit antialiasing (AA) relax the driver and AAF. However, a single-loop CT ΔΣ ADC requires an OSR>16 to be energy efficient. For BW >500MHz, this translates to a very high f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> and, consequently, high power dissipation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".