Digital Background Calibration of Interstage-Gain and Capacitor-Mismatch Errors in Pipelined ADCs
Bibliographic record
Abstract
Two digital background-calibration techniques are proposed to correct for linearity errors due to capacitor mismatches and opamp nonidealities in the pipelined stages of a pipelined analog-to-digital converters (ADC): 1) capacitor-mismatch calibration: the feedback capacitor is randomly swapped with the sampling capacitor(s) in the multiplying digital-to-analog converter (MDAC) of each pipeline stage, during the normal ADC operation. The capacitor-mismatch errors in all stages are then concurrently calibrated in the digital domain. The proposed technique is applicable to both 1.5- and multi-bit MDACs. In a 13-bit pipelined ADC with 0.25% (1sigma) capacitor-mismatch errors, it improves the SNDR from 10 to 12.5 bits and the SFDR from 65 to 95 dB. 2) interstage-gain calibration: Gain errors due to opamp nonidealities in the MDAC of each pipeline stage are modeled using a 4th-order Taylor series expansion of the opamp output and are digitally calibrated. Compared to previously-reported methods for zero- and 2nd-order gain-calibration, the proposed technique for 4th-order gain-calibration reduces the opamp dc gains required to achieve a 13-bit SNDR in a 14-bit pipelined ADC by 22 dB and 9 dB, respectively. Behavioral simulation results are presented
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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 | 0.004 |
| 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) | 0.001 | 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".