Area and Power Optimization of High-Order Gain Calibration in Digitally-Enhanced Pipelined ADCs
Bibliographic record
Abstract
Digital calibration techniques are widely utilized to linearize pipelined analog-to-digital converters (ADCs). However, their power dissipation can be prohibitively high, particularly when high-order gain calibration is needed. This paper demonstrates the need for high-order gain calibration in pipelined ADCs designed using low-gain opamps in scaled digital CMOS. For high-order gain calibration, this paper then proposes a design methodology to optimize the data precision (number of bits) within the digital calibration unit. Thus, the power dissipation and chip area of the calibration unit can be minimized, without affecting the ADC linearity. A 90-nm field-programmable gate array synthesis of a second-order gain calibration unit shows that the proposed optimization methodology results in 53% and 30% reductions in digital power dissipation and chip area, respectively.
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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".