An Ultra-Low-Power Time-Domain Level-Crossing ADC With Adaptive Sampling Rate
Bibliographic record
Abstract
This brief presents a novel ultra-low-power (ULP) time-domain level-crossing (TD-LC) analog-to-digital converter (ADC) with an adaptive sampling rate. By integrating a non-uniform LC sampling technique, the proposed TD-LC ADC further reduces power consumption compared to conventional TD ADCs. A voltage-to-time converter (VTC) is employed to convert the input voltage signal into a time signal, which is then subtracted from a time signal generated by a digital-to-time converter (DTC), converting the digital output from the previous digital output. The time residue determines the necessary adjustment for the digital output. Consequently, the proposed TD-LC ADC achieves 6-bit resolution using only a 3-bit time-to-digital converter (TDC). Fabricated in TSMC’s 0.13-$\mu $m CMOS process, the proposed TD-LC ADC achieves SNDR of 35.4 dB and SFDR of 45.25 dB at 518.31 KHz of BW, and SNDR of 33.59 dB and SFDR of 39.66 dB at 2.07 MHz of BW. The minimum power consumption is 206 nW with a supply voltage of 0.5 V.
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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 | 0.001 | 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) | 0.002 | 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".