Digital background calibration of a 0.4-pJ/step 10-bit pipelined ADC without PN generator in 90-nm digital CMOS
Bibliographic record
Abstract
In nanometer digital CMOS, the linearity of pipelined A/D converters (ADCs) is degraded by the low dc gains of the opamps. Gain-enhancement techniques significantly increase the analog-circuit design complexity at low power and low voltage. Therefore, even in medium-resolution applications, digital background calibration is attractive for designing power-efficient ADCs. A simple, yet accurate, digital background calibration technique, which does not require a pseudo-random (PN) calibration signal, is proposed to minimize the power dissipation in the digital calibration unit. It achieves the same convergence speed and accuracy as PN-based techniques in 2-path (split) pipelined ADCs. A 10-bit 44-MS/s pipelined ADC, fabricated in a standard 1.2-V 90-nm digital CMOS process, uses the proposed calibration technique to achieve a 58.7-dB SNDR for a 21.5-MHz input, with a figure-of-merit (FOM) of 0.4 pJ/step.
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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 | 0.001 |
| 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 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".