Fast digital foreground gain error calibration for pipelined ADC
Why this work is in the frame
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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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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 | 0.001 |
| 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 it