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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".