A 3.97 µW 11.2b 500 kS/s Hybrid SAR ADC via Time-Mode Signal Processing
Bibliographic record
Abstract
This paper presents a 12b hybrid ADC consisting of a 8b successive approximation register (SAR) analog-to-digital converter (ADC) with top-plate sampling, a variable-slope voltage-to-time converter with built-in absolute-value function, and a 3-stage cyclic gated Vernier TDC. The proposed ADC features the ability to accommodate a large input without sacrificing linearity, low power consumption, and technology compatibility. A number of techniques are proposed to improve performance and power efficiency of the ADC. These techniques include a sub-threshold delay-locked loop with pulsed control signals for clock generation, clock kickback reduction to minimize kickback and mismatch induced sensitivity loss of the comparator of the SAR ADC, a gated cyclic Vernier TDC to minimize the resolution loss caused by the improper injection of time inputs and a virtually unlimited dynamic range, a self-resetting arbiter with zero metastability window to improve the resolution of Vernier TDC, and a significantly simplified synchronous SAR for a better power/area efficiency. The ADC is designed in a TSMC 130 nm 1.2 V CMOS technology with a reduced supply voltage of 0.8 V and analyzed using Spectre with BSIM3.3 device models. Simulation results show that at 500 kS/s, the ADC offers a SNDR of 69.04 dB and ENOB of 11.17 while consuming 3.70 µW, yielding a Walden FOM of 3.21 fJ/conv.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".