Digital Background Calibration of Capacitor-Mismatch Errors in Pipelined ADCs
Bibliographic record
Abstract
A digital background calibration technique is proposed to correct for the linearity error due to capacitor mismatches in pipelined analog-to-digital converters (ADCs). During the normal ADC operation, it randomly swaps the feedback capacitor with the sampling capacitor(s) in the multiplying digital-to-analog converter (MDAC) of each pipeline stage in the pipelined ADC. The capacitor-mismatch errors in all pipeline stages are then concurrently measured and corrected in the digital domain. The proposed technique can be utilized in both single-bit and multibit MDACs. Owing to its simple iterative algorithm for capacitor-mismatch error calibration, its implementation requires minimal additional digital hardware. Behavioral simulation results show that, using the proposed calibration technique, the signal-to-noise-plus-distortion ratio is improved from 10 to 12.5bits and the spurious-free dynamic range is increased from 65 to 95 dB, in a 13-bit pipelined ADC with sigma=0.25% capacitor mismatches
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".