Time-Mode All-Digital Delta-Sigma Time-to-Digital Converter with Process Uncertainty Calibration
Bibliographic record
Abstract
This paper studies the impact of process uncertainty on a time-based all-digital △Σ time-to-digital converter (TDC) with a differential pre-skewed bi-directional gated delay line (BDGDL) time integrator. The principle and design of the TDC are presented first. It is followed with an in-depth investigation of the impact of process uncertainty on the building blocks of the TDC. An effective calibration technique capable of minimizing the impact of process uncertainty on the performance of the TSC is proposed. The TDC is designed in a 130 nm 1.2 V CMOS technology and analyzed using Spectre with BSIM4 device models. Simulation results demonstrate that process spread has a significant impact of the delay of the building blocks of the TDC subsequently the performance of the TDC. The detrimental impact of process uncertainty can be minimized by optimizing the TDC at SS (slow NMOS/slow PMOS) corner and adjusting the delay of the key delay blocks and that of the gated delay stages of the TDC in TT (typical NMOS/typical PMOS) and at FF (fast NMOS/fast PMOS) corner to their respective SS-corner value.
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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 | 0.002 |
| 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) | 0.001 | 0.000 |
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".