Fast digital foreground gain error calibration for pipelined ADC
Bibliographic record
Abstract
Here, a fast digital foreground calibration technique to calibrate the gain error in the pipelined analogue‐to‐digital converter (ADC) is proposed. The technique suggested uses maximum reference value of the ADC along with least mean squares adaptive algorithm to compensate the gain error. It avoids the use of slow but accurate reference ADC, thus saving area, power, and design efforts. The proposed calibration algorithm is implemented in Xilinx Artix‐7 FPGA kit to show the effectiveness of the algorithm. After calibration, differential non‐linearity improves by 30% and integral non‐linearity reduces from values +60/−60 LSB to +0.77/–0.77 LSB. Also, signal to noise and distortion ratio and spurious‐free dynamic range improve significantly from 35.9193 and 36.7348 to 75.3619 and 82.2884 dB, respectively, after calibration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".