A 2GS/s 8.5-Bit Time-Based ADC using a Segmented Stochastic Flash TDC
Bibliographic record
Abstract
High-speed (GS/s) low-cost ADCs are of increasing interest for wideband communication systems. While technology helps improve the sampling speed of the ADC, the reduced supply voltage and increasingly complex design rules and device modeling impose great challenges on the dynamic range and design cost of high-speed ADC designs. Time-interleaving (T1) SAR ADCs have shown outstanding power efficiency at high speed [1]. However, the limited singlechannel speed leads to massive Tl channels, which inevitably incur excessive overhead in the sampling network and associated clock generation. Time-based ADCs [2–4] provide a solution to high-speed, medium-resolution conversion with considerably reduced Tl channels and circuit complexity thanks to their fast open-loop operation and the significantly reduced inverter delay in advanced technologies. However, it is still challenging and time-consuming to design a tradition high-precision TDC in the presence of thermal noise and device mismatch. Recently, [5] employed stochastic operation [6] in a time-based ADC to exploit those circuit variabilities, demonstrating reasonably decent ADC performance with design automation. However, such a stochastic ADC architecture typically requires an excessively long chain of delay stages to achieve sufficient random samples for final signal reconstruction, resulting in high accumulated noise that limits the achievable ADC resolution.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".