Bibliographic record
Abstract
Dynamic amplifiers (DAs) have recently emerged as a power-efficient alternative to static current closed-loop operational transconductance amplifiers (OTAs), thanks to their integration-based settling. However, their main drawback remains limited linearity. We present a DA design that cancels expanding and compressing input transistor nonlinearities by exploiting the backgate control of fully depleted silicon-on-insulator (FDSOI) technology. Using an all-region transistor model, harmonic distortion analysis shows that the proposed technique reduces total harmonic distortion (THD) by over 20 dB at an input amplitude of 100 mV. To validate the concept, both a standalone DA and a pipeline-SAR analog-to-digital converter (ADC) integrating the linearized DA as input buffer and residue amplifiers (RAs) were designed and simulated. A novel calibration technique applied to the standalone DA achieved an 18 dB THD reduction at a 200 mVpp, 983 MHz input. Fabricated in GlobalFoundries 22nm FDSOI, the ADC achieved a signal-to-noise-and-distortion ratio (SNDR) of 37 dB at 920 MS/s, consuming 1.8 mW, corresponding to a Walden figure of merit (FOM) of 34.9 fJ/conversion, comparable or superior to similar works. Including the input buffer, the achieved FOM is 68.4 fJ/conversion, outperforming prior designs that include buffer power.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".