Analog Voltage-Shifter for 8-Bit Flash-SAR Hybrid Feed-Forward Architecture-Based ADC
Bibliographic record
Abstract
Power efficiency presents a real bottleneck for highspeed Analog-to-Digital Converters (ADC) designers. In this proposed work, we employ a voltage shifter with a charge-injection (ci) cell-based ADC to perform analog-to-digital conversion alongside a flash ADC with coarse and fine binary searches. The ADC benefits from the small area and low power consumption of the ci-cell Digital-to-Analog Converter (DAC) to accommodate the additional power consumption of the comparators. The 2-bit additional Flash ADC finds the first Most Significant Bits (MSBs). After that, the ci-SAR ADC performs a binary search to find the rest of the bits. In between, the voltage shifts the sample to$\left[\frac{3}{4} V_{\text{DD}}, V_{\text{DD}}\right]$to reduce the SAR ADC's power consumption and improve its linearity. A system-level model is presented for the ADC, alongside a transistor-level simulation for the voltageshifter circuit, which consumes around$35 \mu\mathrm{W}$, with a maximum error of 3 Least Significant Bits (LSB) due to temperature variations from −20° C to 100° C.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".