A High Resolution of MCML-Based Time-to-Digital Converter Implementation
Bibliographic record
Abstract
A new high-resolution time-to-digital converter (TDC) architecture based on MOS-Current-Mode-Logic (MCML) was described.Aiming at high resolution and large dynamic range, the prototype was designed using Virtuoso Cadence Analog Design Environment (ADE) and implemented with 0.13 µm CMOS technology.A minimum time domain resolution of 8.24 pS and a dynamic range of 9 bits were achieved.The capability of operating with variable resolutions was described.With the novel multistep switchable configuration, the TDC is able to operate with four specific resolutions which are 8.24 pS, 10.83 pS, 12.98 pS and 14.3 pS.Each operation mode corresponds to a different power consumption, which makes the new TDC architecture suitable for being embedded in various systems.For instance, a LIDAR is one of the feasible applications.The new TDC designed in this work can serve as the range detection element and provides millimeter-level (2.5 mm, 3 mm, 3.5 mm and 4 mm correspond to the four operation modes) ranging precision in a LIDAR system.The design and implementation were verified through simulation results.iii My deepest gratitude goes first and foremost to Professor Leonard MacEachern, my supervisor, for his constant patience, academic support and invaluable advice.With his guidance the research has been a good experience of my academic career.I would like to express my heartfelt gratitude to my parents, who not only supported me for living in this country, but also comforted me spiritually.Without them this thesis could not have been possible.Also
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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 | 0.000 |
| 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".