Improved binary‐weighted split‐capacitive‐array DAC for high‐resolution SAR ADCs
Bibliographic record
Abstract
An improved split‐capacitive‐array digital‐to‐analogue converter (DAC) with an optimised segmentation degree (i.e. the number of bits in the most significant bit (MSB) sub‐array) is proposed to reduce the area, the switching power consumption and improve the linearity compared to a conventional binary‐weighted (CBW) capacitive‐array DAC and a conventional binary‐weighted split‐capacitive‐array with an attenuation capacitor (BWA) DAC. The presented analysis considers the area and the power dissipation from the DAC as well as the analogue‐to‐digital converter's (ADC's) dynamic performance to determine the optimum segmentation degree for the proposed split‐capacitive‐array DAC and the BWA DAC. Using the minimum matching requirement for the unit capacitor in a 12‐bit CBW DAC, the proposed split‐capacitive‐array DAC with an MSB:LSB = 8:4 segmentation reduces the input capacitance by 2× and reduces the switching power by 15× compared to the 12‐bit CBW DAC. It also improves the ADC's dynamic performance and reduces the switching power by 3.75× compared to the conventional 12‐bit BWA DAC with an MSB:LSB = 10:2 segmentation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".